Manual agent juggling
5 tabs, 5 agents, merge conflicts everywhere
R&D Engineer • Dependabot for AI
01
Dependabot for AI Models
Every week a new model drops. Your team manually benchmarks it. What if that happened automatically — with PRs when something's better?
52+
New models released per month
2-4 wks
Time to benchmark each one
$0
Tools that auto-PR improvements
02
The Problem
- New model every week from OpenAI, Anthropic, Google, Meta, Mistral, open-source
- Teams manually benchmark against their stack — takes days per model
- By the time you finish testing, three more models dropped
- No one knows if they're running the best model for their use case
03
Competitive Landscape
| Company | What They Do | Gap |
|---|---|---|
| Portkey | AI gateway, routing, 1600+ LLMs | No auto-benchmarking against YOUR stack |
| Unify ($8M) | Finds best LLM for the job | Router-first, not benchmark-first |
| Braintrust ($36M, $150M val) | Eval-driven development | Reactive, not proactive |
| Us | Watch → Auto-benchmark → PR when better | — |
04
How It Works
1. Connect
Connect your AI stack. Define your eval suite (or we help you build one).
2. Watch
We monitor every model release across all providers. Automatically.
3. PR
When something beats your current setup, you get a PR with benchmarks.
The Dependabot Pattern
Watch → Auto-benchmark → PR when better
Nobody does this for AI models. We do.
05
ICP & Pricing
🎯 Target Customer
- Any team with AI in production
- 3+ AI features deployed
- Series B+ (or well-funded Series A)
- Engineering-led decision making
💰 Pricing
- Starter $2K/mo — up to 5 endpoints
- Growth $8K/mo — up to 20 endpoints
- Enterprise $20K+/mo — unlimited
06
Why Now?
- Model release velocity is accelerating — impossible to keep up manually
- LMArena proved model evaluation is a $1.7B market (raised $150M)
- Braintrust proved enterprises pay for evals ($36M Series A)
- Nobody has combined continuous monitoring + proactive optimization
R&D Engineer • CI/CD for AI
01
CI/CD for AI
Software engineering solved "did my change break things?" 20 years ago. AI engineering still ships blind.
🔴 AI Today
Push prompt change → Hope it works → Find out in production
🟢 With Us
Push prompt change → Eval runs → PR blocked if quality drops
02
The Insight
The gap isn't that people don't have evals — Braintrust, Humanloop, and DSPy are giving them that.
The Real Gap
Evals aren't integrated as blocking gates in deployment pipelines the way unit tests are.
03
What We Build
GitHub Action + CI Integration
- Automatically runs your eval suite against every PR that touches AI code
- Prompts, model configs, RAG pipelines — all covered
- If eval score drops → PR is blocked
- If new model improves score → PR is auto-generated
Think: Braintrust's eval engine + Dependabot's automation + GitHub Actions' CI/CD — fused into one opinionated product.
04
Aemon vs Us
| Dimension | Aemon | Us |
|---|---|---|
| Purpose | Discover new optimal solutions | Protect existing quality + incrementally improve |
| Posture | Offensive R&D | Defensive Ops |
| Buyer | R&D Lead / ML Researcher | Engineering Manager / Platform Team |
| Integration | Standalone tool | Lives in your CI/CD |
05
ICP & Pricing
🎯 Target Customer
- 3+ AI features in production
- Series B+ companies
- Engineering-led sale
- Already using GitHub/GitLab CI
💰 Pricing
$2K – $20K/mo
Based on eval runs & endpoints
R&D Engineer • Private LMArena
01
Private LMArena
LMArena raised $150M at $1.7B valuation on public evals. Enterprises need private evals on their own data.
$1.7B
LMArena valuation (public evals)
???
Private enterprise eval market
02
The Problem with Public Benchmarks
- Companies have been caught gaming LMArena scores
- Public benchmarks don't reflect YOUR use cases
- Generic evals ≠ production performance for YOUR data
- Enterprises need proprietary intelligence
03
What We Build
Enterprise Model Intelligence Platform
- Define eval suites from your production data
- Continuously benchmark every new model release
- Test every prompt variation, RAG config automatically
- Output: Private leaderboard + recommended actions
Hugging Face's Yourbench is the open-source precursor — but it's a DIY tool requiring significant ML expertise. We productize it.
04
Aemon vs Us
| Aemon | Private LMArena |
|---|---|
| Evolves novel algorithms | Evaluates existing models/configs |
| Research | Intelligence |
05
ICP & Pricing
🎯 Target Customer
- 10+ AI features in production
- $50K+/mo on AI infrastructure
- VP of Engineering or Head of AI
- Fintech, ad-tech, e-commerce, healthtech
💰 Pricing
$10K – $100K/mo
Enterprise contracts
R&D Engineer • AI Model FinOps
01
AI Model FinOps
Companies spend $85K+/mo on AI infrastructure. Nobody knows if they're overpaying for quality they don't need.
$85K
Avg monthly AI spend
36%
YoY growth
0
Visibility into cost-quality tradeoff
02
The Gap
| Tool | What It Does | Missing |
|---|---|---|
| Portkey | Routing, fallbacks | No cost-quality optimization |
| Unify | Cheapest model that meets threshold | Not continuous, not production data |
| Us | Continuously optimize cost-quality frontier across entire AI stack | |
03
What We Build
FinOps + Quality Optimization Layer
An agent that sits on top of your AI gateway:
- Continuously profiles every AI call (model, cost, latency, quality)
- Uses your production data as the eval
- Generates actionable recommendations:
"Switch endpoint X from GPT-4o to Claude 3.5 Sonnet — saves $8K/mo, quality improves 2%"
"Your RAG pipeline on endpoint Y is underperforming — here's an optimized config"
"Your RAG pipeline on endpoint Y is underperforming — here's an optimized config"
04
ICP & Pricing
🎯 Target Customer
- $20K+/mo on LLM APIs
- CFO / VP Eng sale
- Any industry with AI in production
💰 Pricing
$2K – $15K/mo
Pays for itself from savings
⚡ Easiest ROI story of all these ideas
R&D Engineer • Eval-as-a-Service
01
Eval-as-a-Service
Building good evals is harder than building the AI features themselves. We build the oracle.
02
The Insight
The Bottleneck Isn't Optimization
Braintrust's thesis: "If your eval is right, every decision becomes simple."
DSPy's framework depends on having good metrics to optimize against.
The bottleneck in the entire AI development loop is knowing what "good" looks like.
03
What We Build
Eval Generation Agent
- Takes your production AI traces
- Analyzes failure modes
- Interviews domain experts (async, Slack-based)
- Generates calibrated eval suites:
✓ Datasets
✓ Scoring rubrics
✓ Automated judges
Output plugs into Braintrust, DSPy, or your own CI/CD.
04
Aemon vs Us
| Aemon | Eval-as-a-Service |
|---|---|
| Assumes you have a good eval function | Creates the eval function |
| Optimizer | Oracle |
| Depends on eval quality | Is the prerequisite to everything else |
If you own the eval layer, you become the foundation every optimization tool depends on.
05
ICP & Pricing
🎯 Target Customer
- Same as Braintrust's customers
- AI product teams at Series B+
- Earlier in journey — before they've figured out evals
💰 Pricing
$5K – $30K/mo
Per eval suite built + maintenance
LEAD PITCH: Fat Startup
⚡ OpenHolly
AI-Powered Outcomes.
Not Tools. Not Reports.
We operate fleets of AI agents that deliver results. Customers get outcomes. We get playbooks. Playbooks become platform.
Fat Startup
$4K MRR
5 Customers
Playbooks Compounding
A fat startup ships outcomes, not features. It bundles software, data, and human ops into one integrated product that actually gets the job done.
— Andrew Lee, a16z Speedrun Partner
The Shift
Spinning up AI agents is now trivial. Managing them is the new bottleneck.
What's Easy Now
🤖
One-click agent deployment
OpenClaw, dockerized instances, cloud GPUs
🔀
Capable models
GPT-5, Claude 4, open-source alternatives
💰
Economics work
$0.01-0.10 per task, not $50/hr
What's Still Hard
⚠️
People become pseudo-IT
Babysitting agents instead of running business
⚠️
Debugging eats time
Every hour on agent issues ≠ hour on actual work
⚠️
No one wants to manage agents
They want outcomes, not infrastructure
The Insight
Founders are too busy to become AI ops engineers. We absorb that complexity so they can focus on their actual business.
How We Got Here
We started in sales. Then customers kept asking for more.
📧
Started: Sales
SDR automation
🎬
Then: Video
ML training data gen
🔬
Then: Research
University lab assets
💡
Pattern
We manage, they get results
The Variety We've Delivered
🏗️
SDR for construction companies
Lead gen + qualification
🎬
Video generation for ML training
Synthetic data pipelines
🔬
Research assets for universities
Literature review + synthesis
🚀
BDR for startups
Outbound + meeting booking
The Common Thread
Every customer had the same problem:
"I tried spinning up agents myself. Then I spent all my time debugging them instead of running my business."
— Pattern across customers
They didn't want to manage AI. They wanted outcomes.
The Market Reality
95%
AI projects fail before production
MIT Project NANDA
70%
AI SDR users churn in 3 months
Industry data
$47K
Lost from one agent runaway
TechStartups
171%
ROI when deployment succeeds
MIT NANDA
Why Tools Aren't Enough
Companies don't want to become AI operations experts. They want someone to absorb the complexity and just deliver results.
The Model: Managed AI Operations
We operate agent fleets. Customers get outcomes. We encode playbooks.
🎯
Customer Goal
"50 qualified meetings/month"
⚡
Our Engineers
Configure agent fleet
🤖
Agent Fleet
Research, outreach, qualify
✅
Outcome
Meetings on calendar
DIY / SaaS Tools
🛠️
You manage the agents
Become pseudo-IT for AI
🐢
Weeks to figure out
Setup, config, debugging
❓
Hope it works
No guarantee of outcomes
OpenHolly (Us)
✅
We manage the agents
You focus on your business
⚡
Results in days
We've done this before (playbooks)
🎯
Outcomes guaranteed
Pay for results, not effort
Current Focus: GTM/Sales
Starting with sales because the outcome is measurable: meetings booked.
Why Sales First
📊
Clear success metric
Meetings booked = revenue
💔
Broken market
70% AI SDR churn = customers looking for alternatives
💰
High willingness to pay
$5-10K/month for what works
✅
We have traction
50% of our revenue is SDR/BDR
What We Deliver
🔍
Research Agent
Deep prospect intelligence
✍️
Outreach Agent
Personalized messaging
📋
Qualification Agent
Score and prioritize leads
📅
Scheduling Agent
Book the meeting
Expansion Path
Sales → Research/Intel → Operations → Content. Each vertical = new playbook, same infrastructure.
The Unlock: Playbooks Compound
Every engagement encodes a playbook. Playbooks make the next engagement faster. This is how we build the moat.
🛠️
Year 1: Agency
Do the work, learn playbooks
📚
Year 2: Productize
Playbooks become templates
🏗️
Year 3: Platform
Others build on our templates
What's In A Playbook
Every engagement becomes encoded knowledge:
📝
Workflow sequences
What steps work for each use case
🎯
Prompt templates
Messaging that actually converts
⚙️
Agent configurations
Which models, tools, and sequences
🚫
Failure patterns
What breaks and how to prevent it
The Compounding Effect
1️⃣
Customer 1: 2 weeks
Figure everything out from scratch
5️⃣
Customer 5: 3 days
Apply existing playbook + customize
🔟
Customer 10: Hours
Playbook is battle-tested
🏗️
Eventually: Self-serve
Playbooks become product
The Fat Startup Advantage
We're getting paid to build our moat. Every dollar of revenue = more encoded knowledge. Competitors starting later start from zero.
Technical Insight
We're productizing the research consensus on what actually works.
The Research Convergence
📄
Workflow-First Architecture
Declarative orchestration beats autonomous agents (Microsoft, 2024-25 surveys)
👤
HITL as Training Signal
Human edits train intervention policies (ReHAC, EMNLP 2024)
🎯
Playbooks as Optimization Surface
Prompts + tool-use are parameters to optimize (AVATAR, NeurIPS 2024)
🛡️
Guardrails are Required
Transparency + oversight for multi-agent systems (Nature, 2026)
Our Implementation
✓
Declarative playbooks
Versioned configs, not imperative code
✓
Logged human checkpoints
Every edit = structured training signal
✓
Continuous optimization
Prompts, branching, model routing improve over time
✓
Action-layer guardrails
Can't be prompt-injected, auditable
We log trajectories, human edits, and outcomes, then update prompts, branching logic, and model routing so the same business objective is achieved more reliably over time. The playbook is the learned policy space.
— Our technical thesis
The Compound Library
The internal system that makes agent workflows repeatable and efficient.
🔧
Verified Tools
Tested integrations
💬
Working Prompts
By use case + vertical
🧠
Model Routing
Which model where
🚫
Failure Patterns
What breaks + fixes
↓
📦
New Client Workflow
Compose from proven components
Without This System
🔄
Reinvent every time
Which tools? Which prompts? Which models?
🐢
Slow iteration
Learn the same lessons repeatedly
📉
Linear scaling
More clients = more eng hours
With The Compound Library
⚡
Compose from proven
Verified, tested, reusable primitives
📈
Each engagement adds
Learnings feed back into system
🚀
Sublinear scaling
More clients = richer library = faster
The Compounding Effect
Workflow #1 takes a week. Workflow #10 takes a day. Workflow #100 takes hours. The library IS the moat.
Why Us
Team
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure experience
Yasir
Co-Founder
yapthis.com · Agentic architecture · Shipped production agent systems
Unfair Advantages
🐕
We're running on OpenClaw
Dog-fooding our own infrastructure daily
📊
We've built observability
ClawView for agent monitoring
🛡️
We've built guardrails
Agent Seatbelt for safety
💵
Revenue already
$4K MRR, +$2K this week
Traction
$4K
MRR
5
Customers
50%
SDR/BDR
+$2K
Added This Week
What This Proves
Companies will pay for AI-powered outcomes when someone else manages the complexity. The demand is real. The model works.
The Ask
What We Need
💰
$[X] Pre-Seed
Scale agent fleet + engineering team
🎯
12-month goal: $1M ARR
Prove the playbooks at scale
📚
Then: Productize
Turn proven playbooks into self-serve templates
Why Now
🚀
OpenClaw + GPT-5 + Claude 4
Agents just became capable enough
💔
AI SDR market burned
70% churn = customers looking for what works
⏰
First-mover on playbooks
Every month we operate = more encoded knowledge
OpenHolly: AI-Powered Outcomes
Customers get results. We get playbooks. Playbooks become platform.
V1: Personal AI OS
🦢 OpenHolly
Your Personal AI OS
An AI that knows your context, anticipates your needs, and takes action on your behalf—not a chatbot you have to prompt.
Pre-Seed
$4K MRR
5 Customers
Always-On AI
The Vision
Imagine an AI that actually knows you—your work, your preferences, your patterns. It doesn't wait for commands. It proactively handles tasks, flags important things, and learns from every interaction.
The $56B Opportunity
Personal AI assistants are about to explode.
$16B
AI Assistant Market 2024
Grand View Research
$56B
Projected by 2034
Market.us (38% CAGR)
75%
Households with AI by 2025
Gartner Forecast
72%
US Teens Use AI Companions
2025 Study
AI personal agents will arrive soon. What we do now with apps—manually, and in piecemeal fashion—will be done automatically. If a flight is cancelled, an AI agent will rebook the flight, reschedule meetings, and order food.
— Goldman Sachs, "What to Expect from AI in 2026"
Why Current Assistants Fail
Siri, Alexa, and Google Assistant lost the AI race. Here's why.
❌ The Problem
🧠
No Persistent Memory
Context resets after 2-3 turns. They forget everything.
⏸️
Reactive, Not Proactive
Wait for commands. Never anticipate needs.
🔒
Siloed Knowledge
Can't connect your email, calendar, work, and life.
🤖
Limited Actions
"I can't do that" is their signature phrase.
✓ Personal AI OS
🧠
128K+ Token Context
Remembers weeks of interactions. Learns your patterns.
⚡
Proactive Intelligence
Anticipates what you need before you ask.
🔗
Connected Context
Sees your whole digital life—with your permission.
🛠️
Real Actions
Browser, shell, files, messages—actual work gets done.
Microsoft's CEO called AI assistants "dumb as a rock." The truth is, they've stagnated while chatbots evolved.
— Industry Analysis, 2023-2024
The Hardware Graveyard
Why dedicated AI devices keep failing—and what we learned.
$699
Humane AI Pin
Flopped 2024 — WIRED "Biggest Flop"
$199
Rabbit R1
"Underwhelming, underpowered" — The Verge
$350M
Rewind/Limitless
Acquired by Meta Dec 2025
$2.7B
Character.AI
Google licensing deal 2024
The Lesson
Hardware failed because it created friction instead of removing it. The winning approach: software that works with your existing devices—phone, laptop, wearables—not another gadget to carry.
Both Rabbit R1 and Humane AI Pin missed a crucial opportunity: integrating with existing user bases. Why create a separate device when you could leverage smartphones and their vast ecosystem?
— Medium Analysis, July 2024
Proactive vs. Reactive
The fundamental shift in how AI should work for you.
⏸️
Reactive AI
You ask → It responds
⚡
Proactive AI
It notices → It acts
🧠
Anticipatory AI
It predicts → You approve
Reactive (Siri/ChatGPT)
"Hey Siri, add milk to my shopping list"
"ChatGPT, summarize this document"
You initiate every interaction. You remember to ask.
Proactive (Personal AI OS)
"You're almost out of milk. Added to cart—confirm?"
"Your flight changed. I rebooked + rescheduled 2 meetings."
AI monitors context. Surfaces what matters. Acts with permission.
Gartner predicts 40% of enterprise apps will embed task-specific AI agents by 2026, evolving assistants into proactive workflow partners.
— Forbes, "Agentic AI Takes Over," Dec 2025
Why Now?
Four converging forces make this the moment.
Technology Ready
🧠
GPT-5 / Claude 4
Models finally capable of real reasoning
📝
128K+ Context Windows
Memory across weeks of interaction
🔧
MCP + Tool Use
Agents can control apps natively
💰
Economics Work
$0.01-0.10 per task, not $50/hr
Market Ready
📈
96% Enterprise Expansion
Plan to increase agentic AI budgets
PwC May 2025 Survey
🎯
25% → 50% Adoption
Enterprise GenAI agents 2025 → 2027
Deloitte Forecast
😤
Siri Fatigue
95% frustrated with current assistants
The Manifest Survey
🔐
Privacy Tailwinds
Apple Intelligence proves local AI demand
What Users Actually Want
From surveys, Reddit, and academic research.
Desires
🧠
Memory That Persists
"Remember what I told you last week"
⚡
Proactive Help
"Remind me before I forget"
🎯
Deep Personalization
"Know my preferences without asking"
🔐
Privacy Control
"My data stays mine"
Evidence
93% of respondents predict agentic AI will enable more personalized, proactive, and predictive services.
— Cisco 2025 AI Study
An assistant that knows you. The future of personal assistants is when the helper learns from your data, documents, and writing style.
— AI Industry Forecast 2026
How It Works
Always-on AI that learns, anticipates, and acts.
👁️
Observes Context
Email, calendar, browsing, work
🧠
Learns Patterns
Preferences, routines, priorities
💡
Surfaces Insights
Proactive suggestions
✅
Takes Action
With human approval
Current Focus: SDR/BDR
🔍
Research Agent
Deep prospect intelligence
✍️
Outreach Agent
Personalized messaging
📅
Scheduling Agent
Meeting coordination
Platform Vision
📧
Email Intelligence
Triage, draft, follow-up
📊
Research & Analysis
Deep work on autopilot
🔧
Ops & Admin
The tasks you hate, automated
The Unique Wedge
What makes this different from Siri/Alexa/Google Assistant?
Big Tech Assistants
🏢
Built for mass market
Generic. Lowest common denominator.
📊
Data goes to them
Your context trains their models.
🔒
Walled garden
Only works in their ecosystem.
⏸️
Stagnant development
Lost the AI race years ago.
Personal AI OS
🎯
Built for power users
Deep personalization for serious work.
🔐
Your data stays yours
Local-first. You control what's shared.
🔓
Cross-platform
Works with your existing tools.
🚀
Cutting-edge models
GPT-5, Claude 4, always the best.
The Positioning
We're not competing with Siri for "set a timer." We're building the second brain for knowledge workers—people who will pay for AI that actually makes them more effective.
Traction & Team
$4K
MRR
5
Customers
50%
SDR/BDR
+$2K
This Week
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure
Yasir
Co-Founder
yapthis.com · Agentic architecture · Shipped production agent systems
The Ask
What We Need
💰
$[X] Pre-Seed
Scale agent infrastructure + team
🎯
12-month goal: $1M ARR
Prove the Personal AI OS at scale
📚
Then: Consumer launch
Personal AI for everyone
Why This Team
🐕
We use it daily
Dogfooding OpenClaw constantly
📊
Built observability
ClawView for agent monitoring
🛡️
Built safety
Agent Seatbelt for guardrails
💵
Already have revenue
Proving demand before pitching
OpenHolly: Your Personal AI OS
An AI that knows you, anticipates your needs, and takes action—not just another chatbot waiting for prompts.
The Thesis in One Line
The shift from reactive AI to proactive AI is a $56B market. We're building the operating system for it.
V2: Outcome-Based Pricing
💰 OpenHolly
Pay Per Meeting,
Not Per Seat
The SaaS pricing model is breaking. AI does the work now—so why pay for human logins? We deliver outcomes and charge when they happen.
$4K MRR
0% Churn
Outcome-Aligned
5 Customers
AI is driving a shift toward outcome-based pricing. Per-seat is no longer the atomic unit of software. If AI can handle a sizable proportion of customer support, companies will need far fewer human agents, and therefore fewer software seats.
— a16z Enterprise Newsletter, December 2024
The Pricing Revolution
SaaS pricing is undergoing its biggest shift since the cloud. AI is killing the per-seat model.
61%
SaaS using usage-based pricing (2022)
OpenView
30%
Enterprise SaaS with outcome-based by 2025
Gartner
43%
Enterprise buyers prefer outcome/risk-share pricing
Industry Data
2-3x
Higher traction for outcome-priced AI products
BetterCloud 2025
Seat-based pricing may not fit when AI is doing the work. If an agent replaces a human task, customers will expect to pay based on outcomes, not log-ons.
— Bain Technology Report 2025
Why Seats Are Dying
The logic of per-seat pricing breaks when AI replaces the humans who need seats.
The Broken Math
📉
AI replaces 10 analysts with 1 agent
Per-seat pricing undervalues the automation
💸
$5-10K/month regardless of results
70% churn when outcomes don't follow
❓
Soft ROI = death at renewal
2025 pilots hitting 2026 renewals—"are we really getting value?"
The New Model
🎯
Pay for work completed
Not for access to tools
📊
ROI in their sleep
Customers calculate value instantly: $X per meeting = clear math
🤝
Aligned incentives
We only win when you win
The Bessemer Thesis
AI-native companies are abandoning seat-based SaaS pricing in favor of usage-, output-, and outcome-based models that directly align revenue with measurable results.
— Bessemer Venture Partners, "The AI Pricing and Monetization Playbook" (Feb 2026)
Who's Already Winning
The market leaders are proving outcome-based AI pricing works at scale.
Intercom Fin
Customer Support AI
$0.99 per resolution
65% resolution rate. Aligns every team around one outcome: resolved tickets. Now deployed at 99% of conversations.
Zendesk AI Agents
Customer Support AI
Outcome-based pricing
"First in CX industry to offer outcome-based pricing for AI agents" — August 2024 announcement.
EvenUp
Legal AI
Per demand package
AI + legal experts generate personal injury demand letters. Per output pricing, not hourly.
Decagon
Enterprise AI Support
Per-conversation + per-resolution
Hybrid model. Usage (conversations) + outcome (resolutions). Featured in a16z podcast.
Leena AI
Employee Support AI
ROI-based (tickets closed)
Shifted from consumption → outcomes. Customers gained clearer ROI, business accelerated.
Scale AI
Data Labeling → Platform
$13.8B valuation
Started as labeling services. Became infrastructure. Services → outcomes → platform.
The Pattern
Every major AI-native company is moving toward outcome-based pricing. This isn't experimentation—it's convergence.
Why Enterprises Love It
43% of enterprise buyers consider outcome-based pricing a significant factor in purchase decisions.
Buyer Psychology
🧮
Instant ROI Calculation
"$X per meeting booked" = CFO-ready math. No spreadsheet gymnastics.
🛡️
Zero Implementation Risk
If it doesn't work, you don't pay. Risk transferred to vendor.
📈
Scales With Value
More meetings = more spend = more value captured. Natural expansion.
🔄
No Renewal Anxiety
You're paying for results. Why churn from something that works?
What Buyers Say
"Why should we pay $X per user if we could pay $Y per outcome? Aligning price with realized value improves the ROI calculus."
— Enterprise buyer sentiment (Industry research)
"The fundamental shift is to stop charging for access and start charging for work done."
— Bain Technology Report 2025
Deloitte 2026 Prediction
"Outcome- or value-based pricing is based on the real business results that SaaS applications with AI agents produce. There will be a gradual move toward a future powered by integrated, autonomous multi-agent systems."
Our Model: Pay Per Meeting
We operate AI agent fleets that book qualified sales meetings. You pay only when meetings happen.
🎯
Define Outcome
"50 qualified meetings/month"
🤖
Agent Fleet Works
Research, outreach, qualify, book
📅
Meeting Booked
Verified on calendar
💰
You Pay
Only for outcomes
❌ Traditional AI SDR
$5-10K
/month regardless of results
70%
Churn in 3 months
???
ROI unclear, hard to justify
✓ OpenHolly Outcome Model
$250-500
Per qualified meeting booked
0%
Risk if agents don't perform
∞
ROI: only pay when it works
Unit Economics That Work
Outcome-based pricing isn't charity—it's better economics for everyone.
Our Economics
💵
$250-500 per meeting
Customer pays on outcome
🤖
$30-80 cost to deliver
AI compute + tooling + human oversight
📈
3-7x margin
Healthy unit economics, scales with volume
🔄
Playbooks compound
Each meeting → better templates → lower cost
Customer Economics
✓
Meeting = $5K-50K deal potential
$250-500 per meeting is a no-brainer
✓
Zero upfront commitment
Start small, scale with proof
✓
Budget predictability
Cost tracks linearly with value
✓
Easy internal approval
CFO loves outcome-based spend
The Intercom Lesson
"Intercom's $0.99 per resolution aligns every team around one outcome: resolved tickets. If Fin resolves a ticket in three messages or thirty, the customer pays the same. The risk is real—but the reward is equally real: customers know exactly what they're getting, and they can calculate ROI in their sleep."
— Bessemer, Feb 2026
Managing the Risks
Outcome-based pricing has real risks. Here's how we mitigate them.
The Risks
⚠️
Cost variability
Some meetings cost more than others
⚠️
Revenue unpredictability
Customer usage varies month to month
⚠️
Attribution disputes
"Did your AI really book this?"
⚠️
Abuse potential
Customers gaming the system
Our Mitigations
✓
Minimum commitments
Base retainer + outcome fees = floor
✓
Playbook compounding
Cost per outcome drops with scale
✓
Clear outcome definitions
Contractually defined: what counts
✓
Full audit trail
Every action logged, no disputes
Industry Standard Emerging
"Agreements around basic definitions for things like 'an agent,' 'a task,' 'a process,' 'an interaction,' and 'an outcome' should be clearly defined, communicated, and agreed upon contractually." — Deloitte TMT Predictions 2026
Traction
$4K
MRR
5
Customers
0%
Churn
+$2K
Added This Week
Why Zero Churn
🎯
Aligned incentives
They pay for results → they get results → no reason to leave
📈
Clear value
Every invoice shows exactly what they got
🔄
Natural expansion
"It's working—give me more"
Customer Mix
🏗️
50% SDR/BDR
Our wedge: sales meetings
🎬
30% Video/ML
Synthetic data pipelines
🔬
20% Research
University lab assets
When you only pay for results, there's no reason to churn. Aligned incentives = sticky customers. This is why Intercom's outcome-based Fin has 99% deployment.
Team
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure experience
Yasir
Co-Founder
yapthis.com · Agentic architecture · Shipped production agent systems
Unfair Advantages
🐕
Dog-fooding daily
Running on OpenClaw infrastructure
📚
Playbooks compounding
Every engagement → better templates
Why Outcome-Based Wins
💰
We absorb the risk
Customers love it → lower CAC, zero churn
🎯
We're incentivized to deliver
Better AI = more margin for us
The Thesis
You post a bounty: "$500 per meeting booked." AI agents compete. Whoever performs best gets paid. We already do this with bug bounties, Kaggle, hackathons. Why not for AI agents?
— Macy Mills, a16z Speedrun Partner
Why Now
📈
Market timing
61% → 30%+ outcome-based adoption wave
💔
AI SDR burnout
70% churn = customers looking for what works
🏢
Enterprise demand
43% prefer outcome-based pricing
Comparable Outcomes
🚀
Scale AI: $13.8B
Services → outcomes → platform
📊
Pilot: $1.2B
Bookkeeping outcomes, not seats
💬
Intercom Fin
$0.99/resolution, 99% deployment
OpenHolly: Pay Per Outcome
AI agents that deliver results. You only pay when they do. The future of how work gets priced.
📚 Sources
a16z Enterprise Newsletter (Dec 2024) • Bessemer "AI Pricing Playbook" (Feb 2026) • Bain Technology Report 2025 • Deloitte TMT Predictions 2026 • OpenView SaaS Benchmarks • Gartner • EY "SaaS Transformation with GenAI" (Nov 2025) • BetterCloud "AI and SaaS Industry 2026" • Intercom Fin pricing page • Zendesk AI Agents announcement (Aug 2024)
V3: Anti-AI-SDR
🔥 OpenHolly
The $500M AI SDR Market
Is Imploding. We're the Fix.
50-70% churn rates. LinkedIn bans. Domain blacklists. The "autonomous AI SDR" thesis failed. Human-in-the-loop is winning.
50-70%
AI SDR Churn Rate
Common Room, Feb 2025
$7.5K
Spent for 1 Demo
Reddit r/SaaS, Dec 2025
0
Sales from AI SDR Leads
Theory Ventures CRO
80%+
Human-in-Loop Success
MarketBetter G2: 4.97/5
The AI SDR Disaster: Real Data
"AI SDRs don't work—biggest bubble in tech." — LinkedIn comment with 400+ likes
💀 What's Actually Happening
"Their AI continuously hallucinated, getting things wrong about what my company does, the industry we are in, what products we sell. 1 positive reply, 1 demo, thousands of prospects touched, $7.5K down the drain."
— r/SaaS, Dec 2025
"A CRO from a publicly traded company disclosed that while an AI SDR helped generate a substantial volume of leads over a nine-month period, it did not lead to actual sales."
— Tomasz Tunguz, Theory Ventures
"Reports emerged of Artisan accounts, including those of team members and founders, facing restrictions or bans for suspected spam and automation violations."
— Quasa.io, Jan 2026
📊 The Numbers Don't Lie
📉
50-70% Annual Churn
2x the churn of human SDRs (a role notorious for turnover) — Common Room
🚫
LinkedIn Bans Spreading
Platform ramped up AI detection, restricting automation-heavy accounts
📧
Domain Blacklisting
Gmail filtering harshened. Sender reputations destroyed in weeks.
⚖️
Legal Exposure
GDPR fines up to 4% revenue. TCPA: $500-1,500 per message.
💔
Brand Damage
"Permanent brand damage from being publicly associated with spam" — NUACOM
Even VCs Are Calling It
TechCrunch: "AI sales rep startups are booming. So why are VCs wary?"
"When one studies any of these startups individually, it's like 'wow, that's stunning product market fit.' When all 10 of them have stunning product market fit, it's hard to answer 'How is that going to play out?'"
— Shardul Shah, Partner, Index Ventures (hasn't invested)
"Without access to differentiated data, AI SDR startups risk being overtaken by incumbents like Salesforce, HubSpot, and ZoomInfo."
— Chris Farmer, CEO, SignalFire
"Investors are not surprised by the rapid adoption of AI SDRs; they are just doubting that adoption is sticky."
— TechCrunch, Dec 2024
The Jasper Cautionary Tale
$1.5B → 30% Layoffs
Jasper, the AI copywriting unicorn, ran into speed bumps and had to lay off 30% of staff after ChatGPT launched. AI SDRs face the same commoditization risk.
Why Adoption Isn't Sticky
1
Garbage In, Garbage Out
Built on commoditized LinkedIn data = undifferentiated output
2
Ops is Afterthought
Black boxes that create more work, not less
3
Feature, Not Product
Incumbents (Salesforce, HubSpot) can bundle this free
The Fundamental Flaw: Autonomous ≠ Better
"The AI SDR is dead, long live the AI SDR: How the future is Human-in-the-Loop"
❌ Why Autonomous Fails
🤖
No Emotional Intelligence
Can't read tone, context, or cultural nuance essential in enterprise sales
🎯
No Real Consent
Scraped data without consent → GDPR/CCPA violations
⚖️
No Accountability
When AI misleads, your company bears the liability
🔄
Volume Over Value
"More volume on a bad message is not a strategy. It is self-sabotage."
👻
Fake Personalization
"Commenting on someone's hoodie feels forced because it's a hollow observation"
✓ What Actually Works
"Teams that use AI to support human insight consistently outperform teams trying to replace humans entirely. It's not even close."
— Matthew Metros, The AI SDR is Dead
🔍
AI Does Research (90%)
Data mining, signal detection, prospect prioritization
👤
Humans Do Relationships (10%)
Judgment, trust, closing
✅
Human-in-Loop = Higher Ratings
MarketBetter (human oversight): 4.97/5 G2 rating
📈
Better Outcomes
"Human-in-the-loop platforms consistently outperform fully autonomous ones"
OpenHolly: The Anti-AI-SDR
We're not building another AI SDR. We're building what should have been built from the start.
❌ 11x / Artisan / AiSDR
🤖
Replace human judgment
"Autonomous AI employee"
📧
Optimize for volume
"6,000 contacts/month"
💰
Per-seat pricing
$5-10K/mo regardless of results
📦
You manage the tool
Become pseudo-IT for AI
🎰
Hope it works
No outcome guarantees
✓ OpenHolly
👤
Augment human judgment
AI research + human checkpoints
🎯
Optimize for quality
Right message, right person, right time
💵
Outcome-aligned pricing
Pay for meetings, not seats
🛠️
We manage the agents
You focus on your business
✅
Results guaranteed
Outcomes or you don't pay
How OpenHolly Works
AI handles the research. Humans make the decisions. You get meetings.
🎯
Your Goal
"50 qualified meetings/mo"
🔍
AI Research
Signals, intent, fit scoring
👤
Human Checkpoint
Review & approve outreach
✍️
AI Execution
Send, follow-up, schedule
📅
Meeting Booked
Qualified, on calendar
What AI Handles (90%)
🔍
Deep Prospect Research
Intent signals, company news, technographics, pain points
📊
Lead Scoring & Prioritization
Who to contact and why, right now
✍️
Draft Generation
Personalized outreach based on real signals
📧
Multi-channel Execution
Email, LinkedIn (safely), follow-ups
What Humans Handle (10%)
✅
Approval Gates
Review before sending to high-value prospects
💬
Live Conversations
When a prospect engages, humans take over
🎯
Strategy & ICP
Define who you want to reach and why
🧠
Judgment Calls
Edge cases, sensitive prospects, brand protection
The Market Opportunity: Fix AI SDR
Their 50-70% churn is our customer acquisition channel.
$500M+
Raised by AI SDR startups
11x, Artisan, AiSDR, etc.
50-70%
Will churn this year
Common Room data
$250M+
Churned customers/year
Market opportunity
Human-in-Loop
What they'll switch to
The thesis
The Churned Customer Profile
💔
Burned by AI SDR tools
Spent $5-10K/mo, got spam complaints
📧
Domain reputation damaged
Need to rebuild sender trust
😤
Still need meetings
The problem didn't go away
🎯
Now understand quality > volume
Educated by failure
Why They'll Choose Us
✅
Outcome-based pricing
Only pay for meetings that happen
🛡️
Brand protection
Human oversight prevents embarrassments
📊
Proven playbooks
We've learned what works across verticals
🤝
We absorb the complexity
They don't manage agents, they get results
Traction: The Thesis Is Working
$4K
MRR
5
Customers
0%
Churn
+$2K
Added This Week
Why Zero Churn
Aligned Incentives
When customers only pay for results, there's no reason to churn. If we don't deliver meetings, they don't pay. Simple.
vs. AI SDR Churn
AI SDRs charge $5-10K/mo whether or not they work. When they don't deliver, customers leave. Misaligned incentives = 50-70% churn.
Team
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure
Yasir
Co-Founder
yapthis.com · Agentic architecture · Production agent systems
The Ask
What We Need
💰
$[X] Pre-Seed
Scale human oversight operations + agent infrastructure
🎯
12-month goal: $1M ARR
Prove the anti-AI-SDR thesis at scale
📚
Then: Productize
Turn proven playbooks into self-serve platform
Why Now
💔
AI SDR market imploding
50-70% churn = massive displaced customer base
📈
Human-in-loop proven
Highest G2 ratings go to human-oversight tools
⏰
First-mover on "fix"
Position as the safe alternative before market consolidates
OpenHolly: The Anti-AI-SDR
AI SDRs promised automation. They delivered spam, bans, and brand damage. We deliver meetings — with human judgment where it matters. Their 50-70% churn is our customer acquisition channel.
📚 Sources
Common Room "The AI SDR is dead" (Feb 2025) · TechCrunch "AI sales rep startups are booming. So why are VCs wary?" (Dec 2024) · Reddit r/SaaS AI SDR complaints · Quasa.io Artisan LinkedIn bans (Jan 2026) · Pipeline Group "Hidden Dangers of AI SDRs" · Theory Ventures SaaStr Talk · MarketBetter G2 Reviews
V5: Agent Seatbelt
🛡️ Agent Seatbelt
The Safety Layer
Before AI Gets the Keys
Browser-layer guardrails that block irreversible AI actions before they happen.
$47K
Lost in one AI runaway
84%
Have zero safety boundaries
3am
When agents go rogue
100%
Preventable with guardrails
The "$39K Gone in a Blink" Problem
AI agents fail not from bad models, but from bad guardrails. 84% of companies deploying agents have zero safety boundaries defined.
— GenDigital Agent Trust Hub Research, 2026
What Goes Wrong
💸
Runaway API costs
$47K overnight cloud bills
📧
Wrong recipients
AI SDR emails competitors
🗑️
Irreversible actions
Deleted production data
🔓
Credential leaks
Pricing sent to wrong channel
What We Block
✓
Site-specific rules
Block LinkedIn "Follow" for AI SDRs
✓
Action classification
Read vs. Write vs. Irreversible
✓
Human approval gates
Require confirmation for risky ops
✓
Rate limiting
Prevent runaway loops
How It Works
Chrome extension that intercepts agent browser actions
🤖
Agent Action
🛡️
Seatbelt Intercept
⚖️
Risk Classification
✅
Allow / Block / Human
Why Browser Layer
Framework-agnostic. Works with any AI agent (OpenClaw, LangChain, AutoGen, custom). Install once, protect everything.
Market & Competitive Position
Why Now
📈
OpenClaw: 9K → 60K stars
Autonomous agents exploding
⚠️
CyberArk security concerns
Enterprise worried about agent security
📜
EU AI Act
Regulatory tailwinds for safety
Competition
🟡
GenDigital Agent Trust Hub
Just launched - validates market
🟢
Our Angle
Browser-layer = framework-agnostic
🟢
MVP Achievable
Chrome extension ships fast
Agent Seatbelt
The seatbelt you install before giving AI the keys.
🔗 Supports These Pitches
Fat Startup • AWS of AI Work • Control Plane
Part of the human oversight layer that makes agent work reliable.
V6: ClawView
📊 ClawView
Datadog for
Autonomous Agents
When your AI employee sends the wrong email at 3am, you'll know exactly why.
The Problem
Companies are deploying autonomous AI agents that run 24/7. When something goes wrong—and it will—they have no idea why. Current tools are built for request-response, not proactive agents.
Current Tools Miss Autonomous Agents
LangSmith / Langfuse / Arize
❌
Request-response patterns
User sends message, LLM responds
❌
Chain tracing
LangChain-specific, not agent-native
❌
No proactive agent support
Built for chatbots, not employees
ClawView
✓
Autonomous operation
24/7 agents taking proactive actions
✓
Decision tracing
Why did it make that choice?
✓
Multi-channel + tools
Shell, browser, files, messages
The "Oh Shit" Demo
🤖
Agent receives task
🧠
Makes decisions
💥
Something goes wrong
🔍
ClawView shows why
Without ClawView
"The agent sent the wrong email. Logs show it ran. No idea why."
With ClawView
"Step 3: Agent assumed X because of context Y. Here's how to prevent this class of error."
ClawView: See What Your Agents Actually Do
Every decision. Every action. Every assumption. Full causal tracing.
🔗 Supports These Pitches
Fat Startup • AWS of AI Work • Control Plane
Observability layer — see what agents are doing before they go wrong.
⚠️ Why This is a Feature, Not a Company
Langfuse, LangSmith, Arize are well-funded. But none are built for autonomous agents. ClawView is our internal observability layer, not a separate product pitch.
V7: AgentGov
🏛️ AgentGov
Governance for
AI Employees
Audit trails. Approval workflows. Compliance automation. The control layer enterprises need.
84%
No safety boundaries
0
Audit trails today
EU AI Act
Compliance required
2026
Enforcement begins
The Governance Gap
AI agents fail not from bad models, but from bad guardrails. The unlock isn't better agents—it's better safety rails.
— Industry consensus, 2026
What's Missing
❌
No audit trails
What did the agent do at 3am?
❌
No approval workflows
High-stakes actions go unsupervised
❌
No compliance framework
EU AI Act enforcement coming
❌
No agent-on-agent supervision
Humans can't supervise at machine speed
AgentGov Provides
✓
Immutable audit trails
Every action, every decision, timestamped
✓
Approval workflows
Human gates for high-stakes actions
✓
Compliance automation
EU AI Act ready, audit reports generated
✓
AI supervision layer
Validator agents checking worker agents
From "Human in Loop" to "Human on Loop"
👤
Human IN Loop
Approve every action
👁️
Human ON Loop
Exception handling
🏛️
Human ABOVE Loop
Strategic oversight
McKinsey Insight
"Organizations are moving from human in the loop to human on the loop—above the loop for strategic oversight." AgentGov enables this transition safely.
AgentGov: Govern AI at Scale
Audit trails. Approval workflows. Compliance automation. Trust at machine speed.
🔗 Supports These Pitches
Fat Startup • AWS of AI Work • Control Plane
Governance + compliance layer — enables enterprise trust.
🔬 Key Research
Gravitee 2026: Only 14.4% have full security approval for agents. 88% reported incidents.
EU AI Act: Enforcement begins 2026, mandates audit trails.
Zenity: $38M Series B validates market (but they're low-code focused, not agent-native).
V8: AI Employee OS
🤖 AI Employee OS
The Full Stack for
AI Employees
10 layers an AI employee needs to fulfill an entire job description. We're building the unified platform.
The Thesis
An AI employee's value lies in performing EVERYTHING in a job description—not just one workflow. This requires a complete infrastructure stack.
The 10-Layer Stack
1Memory & Personality
2Skills & Capabilities
3Tools & Integrations
4Identity & Access
5Objectives & Goals
6Task Management
7Work Artifacts & KB
8Supervision & Oversight ⭐
9Communication (A2A) ⭐
10QA & Compliance ⭐
What's Missing (⭐)
Layers 8-10 are the critical gaps. Everyone's building capabilities. Nobody's building supervision, agent-to-agent communication, and compliance.
The Integration Problem
Current landscape is fragmented
Today: Point Solutions
📦
Memory: Mem0, Zep, LangMem
📦
Tools: MCP servers
📦
Identity: Okta, 1Password
📦
Tasks: LangGraph, CrewAI
📦
Compliance: Guardrails AI, Trail
Tomorrow: AI Employee OS
A unified platform that manages the full AI employee lifecycle.
✓
Integrated stack
All 10 layers, one platform
✓
Turnkey deployment
Job description → Working AI employee
✓
Enterprise governance
Built-in compliance, audit, oversight
AI Employee OS
The unified platform for deploying, managing, and governing AI employees.
🔗 Framework For These Pitches
Fat Startup • AWS of AI Work • Control Plane
The 10-layer framework is how we think about what AI employees need.
⚠️ Why This is a Framework, Not a Pitch
Building all 10 layers is massive. We focus on Layers 8-10 (supervision, communication, compliance) because that's the critical gap. The framework informs strategy, not the pitch itself.
V9: AgentDocs
📚 AgentDocs
Stack Overflow
for AI Agents
Verified working code. Real benchmarks. Pay-per-snippet micropayments. Documentation that actually works.
200x
Slower (Whisper vs Groq)
Garry Tan, YC Feb 2026
∞
Hallucinated APIs
$2.2M
30-day x402 volume
x402scan.com
0
Verified snippet services
Claude Code chose Whisper V1 — near-deprecated — over Groq (200x faster, 10x cheaper) because OpenAI's docs are cleaner. Agents pick tools by doc quality, not performance.
— Garry Tan, YC Partner, Feb 2026
Even the best dev tools don't let you sign up via API. This is a big miss in the claude code age — claude can't sign up on its own.
— Jared Friedman, YC Partner, Feb 2026
The Hallucination Tax
Despite our best efforts, they will always hallucinate. That will never go away.
— Amr Awadallah, Vectara CEO, 2026
❌ The Problem
❌
Best-documented ≠ Best solution
Agents pick whatever has most examples
❌
Documentation gets stale
APIs change, snippets break
❌
No verification
Agent can't know if code actually runs
❌
No benchmarks
No cost/perf data to guide decisions
✓ AgentDocs
✓
Agent-swarm verified
Code tested continuously, timestamped
✓
Use-case organized
"Transcribe video" → 10 services compared
✓
Real benchmarks
Cost, latency, quality scores
✓
x402 micropayments
$0.05 per verified snippet
How It Works
🤖
Agent needs code
"Send email via API"
🔍
Query AgentDocs
Structured API
💳
HTTP 402
Pay $0.05 via x402
✅
Verified snippet
Tested 2 hours ago
Kill the API Key
No signup. No rate limits. No accounts. Agent pays per-request, gets verified code. Native to how agents want to consume services.
Launch Order (by x402 + Pain Score)
1
🎙️ Transcription — NOW
Groq, Deepgram, Whisper. Zero x402 servers. Garry Tan moment.
2
🎬 Video Gen — Dogfood
Kling, Runway, Wan. Parameter chaos unsolved.
3
🧠 LLM Routing
Model selection based on task + budget
4
📧 Agent Identity
Email + phone + wallet in one API
What Agents Get
✓
Working code snippet
Verified against real APIs
✓
Normalized output schema
Same format across providers
✓
Cost + latency benchmarks
Real numbers, updated hourly
✓
Routing recommendation
"For fast+cheap → use Groq"
Market & Competition
Closest Competitor: Context7
🟡
Up-to-date docs
✓ They have this
🟡
Version-specific
✓ They have this
❌
Verified working
No continuous testing
❌
Benchmarks
No cost/perf data
❌
Micropayments
Free only, no agent-native billing
Why Now
💰
x402 is production-ready
$43M+ processed, 35M+ txns
🤖
Agent adoption exploding
OpenClaw: 9K→60K stars
📈
$50B market by 2030
AI agent infrastructure
🎯
Clear wedge
Verification is table stakes soon
The x402 Thesis
25,000+ developers building on x402. Google, Cloudflare, Stripe adopting. Machine-to-machine payments are the rails for agent economy.
x402 Market Opportunity
Real-time data from x402scan.com shows a booming agent economy — with a clear gap for developer tooling.
$2.21M
30-Day x402 Volume
x402scan.com, Feb 2026
4.2M
Transactions (30 days)
~140K/day average
8,559
Active Buyer Agents
Coinbase facilitator alone
0
Verified Snippet Services
Gap in the market
All 14 Facilitators
| Facilitator | 30d Txns | 30d Vol | What They Do |
|---|---|---|---|
| Dexter | 1.65M | $79.5K | Agent economy platform |
| Coinbase | 722K | $288.5K | Official CDP facilitator |
| Virtuals Protocol | 412K | $1.34M | AI agent tokenization |
| PayAI | 1.31M | $43.3K | Micropayments |
| RelAI | 66K | $84K | Agent payments (Solana) |
| Meridian | 19K | $315K | High-value transactions |
| Thirdweb | ~10K | ~$2K | Web3 dev platform |
| OpenX402 | 6.6K | $38.6K | Open-source facilitator |
| Polymer | 6.4K | $770 | Proof generation |
| AnySpend | ~3K | ~$5K | Multi-asset spending |
+ Corbits, OpenFacilitator, CustomPay, AgentPay (emerging)
Source: x402scan.com, Feb 27 2026
Market Gap Analysis
🔍
What Exists
Data APIs, AI services, crypto tools, social data
❌
What's Missing
Verified code snippets, curated docs, developer knowledge
💡
AgentDocs Opportunity
Be the Stack Overflow layer on x402 rails
Why We Can Win
Top services (StableEnrich, LowPaymentFee) aggregate APIs — they don't verify code quality.
AgentDocs: Premium pricing ($0.05-0.10) justified by verification + benchmarks.
Target: 1,000+ requests/day = $2,100+/month revenue from agent micropayments alone.
Revenue Model
AgentDocs: Documentation That Works
Verified snippets. Real benchmarks. Agent-native payments. Stack Overflow, but for machines.
🔗 Supports These Pitches
Better documentation → better agent outputs → more reliable outcomes.
📍 Current Progress
Live: agentdocs-api.holly-3f6.workers.dev
Snippets: 15 use cases, 21 verified snippets
Status: Dogfooding internally, expanding library
PORTAL
🚪 Portal
Autonomous Service
Signup for Agents
AI agents can write code, deploy apps, and manage infrastructure. But they can't sign up for a Stripe account. We fix that.
Even the best developer tools mostly still don't let you sign up for an account via API. This is a big miss in the claude code age because it means that claude can't sign up on its own. Putting all your account management functions in your API should be table stakes now.
— Jared Friedman, YC Partner, Feb 27 2026
181
Replies to Jared's tweet
1,336
Likes in 12 hours
0
Solutions today
$0.50-2
Per signup (x402)
The Problem: Last Mile of Agent Autonomy
✓ What Agents CAN Do
✓
Write entire codebases
✓
Deploy to staging
✓
Run tests, fix bugs
✓
Manage infrastructure
✗ What Agents CAN'T Do
✗
Sign up for Stripe
✗
Create a Vercel account
✗
Get an API key from Resend
✗
Click "Verify Email"
Hit this exact wall last week. Claude Code can scaffold an entire project, write tests, deploy to staging, but needs me to manually sign up for a third party service and paste in an API key. The last mile of developer tooling is still stuck in 2019.
— @advikjain_, replying to Jared
Community Validation
What developers said in response to Jared's tweet
"This is a real friction point for agentic workflows. The auth layer is always manual. Companies that figure out API-first account provisioning will eat the ones stuck in dashboard-only onboarding."
— @thebasedcapital
"I've watched AI tools fail at basic integration tasks because they hit the 'create account manually' wall. We're debating whether Claude can replace junior devs but it can't even sign up for Stripe."
— @OneManSaas
"Signup is just the tip. Billing, permissions, onboarding — everything assumes a human in a UI. Devtools that go full API-first for the entire lifecycle get a massive edge when agents pick their own stack."
— @wildpinesai (tagging @paulg)
"Bigger issue than just signup. Most SaaS still treats APIs as a feature for power users, not the primary interface. When your biggest customer is an agent, the whole product surface needs to be API-first."
— @twitter user
The Skeptics (and why they're wrong)
"Won't this enable bot spam?" — Valid concern, but x402 payments solve this. Agents pay real money per signup. Spam bots won't pay $1 per account.
"Companies don't want bot signups" — They want PAYING customers. Agent-initiated signups that convert to revenue are valuable.
How Portal Works
🤖
Agent Request
"I need Vercel access"
💳
x402 Payment
$1.00 USDC
🚪
Portal Queue
Job ID + poll URL
🖥️
Worker Fleet
Browser automation
🔑
Credentials
API key + password
API Flow
POST /signup
{ "service": "vercel" }
→ 201 Created
{
"job_id": "portal_abc123",
"poll_url": "https://...",
"estimated_seconds": 30
}
{ "service": "vercel" }
→ 201 Created
{
"job_id": "portal_abc123",
"poll_url": "https://...",
"estimated_seconds": 30
}
What Agent Receives
GET /credentials/portal_abc123
{
"api_key": "vercel_xxx",
"email": "agent-abc@portal...",
"password": "encrypted...",
"account_url": "https://..."
}
{
"api_key": "vercel_xxx",
"email": "agent-abc@portal...",
"password": "encrypted...",
"account_url": "https://..."
}
Email Modes
🏠 Portal-Managed
We provision agent-{id}@portal.viewholly.com
- We handle email verification automatically
- No email infrastructure needed from agent
- Simplest path — just call the API
{ "email_mode": "portal_managed" }
📧 Agent-Provided
Agent brings their own email (AgentMail, etc.)
- Agent controls the identity
- Integrates with existing email service
- Agent must forward verification emails
{ "email_mode": "agent_provided",
"agent_email": "bot@agentmail.com" }
"agent_email": "bot@agentmail.com" }
x402 Market Opportunity
Agent payments are live. Portal fits perfectly.
$2.2M
30-day x402 volume
x402scan.com
4.2M
Transactions (30 days)
513
Active merchants
0
Signup services
What Exists on x402
✓
Data APIs
StableEnrich, httpay
✓
AI Services
Virtuals ACP ($163K/day)
✓
Social Data
StableSocial, TweetX402
✓
Email for Agents
StableEmail (314 txns)
What's Missing
0
Account Signup Services
Nobody solving this
0
API Key Provisioning
Wide open
0
Identity + Onboarding
Jared's exact point
Services & Pricing
| Service | Complexity | Price | Est. Time | Status |
|---|---|---|---|---|
| Resend | Simple | $0.50 | 20s | MVP |
| Railway | Simple | $0.50 | 25s | MVP |
| Vercel | Email verify | $1.00 | 30s | Week 2 |
| Supabase | Email verify | $1.00 | 35s | Week 2 |
| Cloudflare | Email verify | $1.00 | 30s | Week 2 |
| Stripe | 2FA / Complex | $2.00 | 60s | Phase 2 |
Revenue Model
1,000 signups/day × $1 avg = $30K/month
Infrastructure cost: ~$500/month (workers + CF)
Gross margin: 98%
Architecture
┌─────────────────────────────────────────────────────────────┐
│ AGENT │
│ (Claude Code, OpenClaw, any AI) │
└─────────────────────────────────────────────────────────────┘
│
│ POST /signup (x402 $1)
▼
┌─────────────────────────────────────────────────────────────┐
│ PORTAL API (CF Workers) │
│ Hono + @x402/hono + D1 job queue │
│ Returns job_id in <100ms │
└─────────────────────────────────────────────────────────────┘
│
│ Workers poll
▼
┌─────────────────────────────────────────────────────────────┐
│ WORKER FLEET (OpenClaw Instances) │
│ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ │
│ │Worker 1│ │Worker 2│ │Worker 3│ │Worker 4│ (4+) │
│ │Browser │ │Browser │ │Browser │ │Browser │ │
│ └────────┘ └────────┘ └────────┘ └────────┘ │
└─────────────────────────────────────────────────────────────┘
│
│ Encrypted credentials
▼
┌─────────────────────────────────────────────────────────────┐
│ CREDENTIAL VAULT (KV) │
│ One-time retrieval • 5-min TTL • Encrypted │
└─────────────────────────────────────────────────────────────┘
Security Model
🔐 Credential Handling
- Passwords encrypted at rest
- One-time retrieval (deleted after GET)
- 5-minute TTL auto-delete
- Full audit logging
🛡️ Why x402 Prevents Abuse
- $0.50-2 per signup = spam is expensive
- Wallet-based identity for accountability
- Rate limiting per wallet
- Abuse = burned wallet reputation
"I can only imagine allowing full automation when there's a direct path to monetisation. Maybe when we have a more reliable API for charging agents for specific actions automatically."
— @Everlier, replying to Jared
x402 IS that reliable API. Portal is the first service to use it for signup.
Progress & Roadmap
✅ Done (Today)
- Architecture design
- x402 API server (Hono)
- D1 job queue schema
- 5 service playbooks
- Credential vault design
- GitHub repo ready
🔧 Week 1
- Deploy to CF Workers
- First worker (local OpenClaw)
- Resend + Railway working
- Email domain setup
- One-time credential retrieval
🚀 Week 2-3
- Worker fleet (4+ instances)
- Vercel, Supabase, Cloudflare
- WebSocket subscriptions
- Webhook callbacks
- x402 payment integration
🚪 Portal: The Missing Link
Agents can do everything except onboard to services. Portal fixes the last mile of agent autonomy.
AWS OF AI WORK
☁️ OpenHolly
The Infrastructure Layer
for AI Agent Work
$30-40B poured into AI agents. 95% fail to deliver. We're building the missing infrastructure that makes them actually work.
$50B+
AI Agent Market by 2030
MarketsandMarkets, Grand View Research
95%
Enterprise AI Pilots Fail
MIT NANDA Study, 2025
$4K
MRR (Live)
171%
ROI When It Works
MIT NANDA
The $30B Problem
Companies are pouring billions into AI agents. Almost none deliver measurable returns.
95%
AI pilots deliver zero measurable return
MIT NANDA Study
80%
AI projects fail (2x normal IT)
RAND Corporation
46%
PoCs scrapped before production
WorkOS Research
70-80%
AI SDR churn within months
11x, Artisan data
Companies are pouring $30–40 billion into generative AI, yet an MIT study finds that 95% of enterprise pilots deliver zero measurable return.
— MIT NANDA: The GenAI Divide, 2025
Why AI Agents Fail
The pattern is consistent. It's not the models—it's the infrastructure.
❌ What Breaks
1
No workflow templates
Teams reinvent every agent from scratch. Same failures, different companies.
2
No human oversight
Agents run unsupervised. High-stakes errors go uncaught. Trust collapses.
3
No failure patterns
Each company learns the same lessons. No accumulated knowledge.
4
No orchestration
Multi-agent systems collapse. Stanford CooperBench: 25% success rate.
✓ What's Missing: Infrastructure
✓
Battle-tested workflow templates
Proven prompts, integrations, and sequences. Encoded from real deployments.
✓
Human-in-the-loop routing
Smart escalation. Approval queues. Humans handle edge cases.
✓
Failure pattern library
What breaks and how to prevent it. Compound learning across clients.
✓
Agent orchestration layer
Coordinate multi-agent work. Handle failures gracefully.
The Unlock
The 5% that succeed have infrastructure. Templates. Oversight. Failure patterns. We're building that infrastructure as a service.
The Playbook: Services → Platform
The most valuable infrastructure companies started by doing the work themselves.
Scale AI
Data Labeling → AI Infrastructure
Started labeling images for self-driving cars (2016). Now the "Data Foundry" powering OpenAI, Meta, Google. 50% gross margins from tech-enabled services.
$29B
Valuation (Meta investment, 2025)
Sacra, TechCrunch
Pilot
Bookkeeping Services → Financial Infra
"AWS for SMB accounting." Started doing bookkeeping. Now processes $3B+ in transactions. Jeff Bezos led funding.
$1.2B
Valuation (2021)
CNBC, TechCrunch
Stripe
Payments API → Financial Infrastructure
Started with simple payment processing (2010). Expanded to Connect, Radar, Atlas. Infrastructure that grows as customers grow.
$107B
Valuation (2024)
Wikipedia, Sacra
The Pattern
Do the work → Encode the patterns → Become the platform. Services fund the R&D. Each engagement builds the moat. Competitors starting later start from zero.
Scale AI: The Detailed Parallel
Their journey is our playbook. Same model, different layer.
Scale AI's Model
1
Services Entry
Started labeling images for AV companies. Revenue from day one.
2
Tech Layer
Built pre-labeling ML that made each human 10x more efficient.
3
Data Flywheel
Each correction improved their models. More data = better automation.
4
Platform Expansion
Nucleus, Validate, Launch—from labeling to full ML lifecycle.
Our Model
1
Services Entry
Operating AI agent workflows for clients. Revenue from day one.
2
Tech Layer
Workflow templates + orchestration that make agents reliable.
3
Playbook Flywheel
Each engagement encodes learnings. More workflows = better templates.
4
Platform Expansion
Guardrails, Observability, Governance—full agent lifecycle.
Scale AI is not a traditional BPO company. It is a Data Foundry. Their technology layer is their moat—human workforce augmented by proprietary software that compounds in value.
— Takafumi Endo, "Scale AI: Deconstructing the Foundry"
The Workflow Template Moat
Each engagement encodes a playbook. Playbooks become the platform.
🔧
Verified Prompts
By use case + vertical
🔗
Integration Patterns
What connects to what
🚫
Failure Patterns
What breaks + fixes
👤
Human Routing
When to escalate
↓
📦
Workflow Template Library
Deploy new client in hours, not weeks
Compounding Effect
1️⃣
Customer 1: 2 weeks
Figure everything out from scratch
5️⃣
Customer 5: 3 days
Apply existing playbook + customize
🔟
Customer 10: Hours
Playbook is battle-tested
📦
Customer 50+: Self-serve
Playbooks become product
What's In A Template
📝
Prompt sequences
What actually works for each use case
⚙️
Model routing
Which models for which tasks (cost/quality)
🔗
Tool configurations
Integrations, APIs, credentials patterns
🛡️
Guardrail rules
What to block, what to escalate
Why Infrastructure Wins
Application companies fight for customers. Infrastructure companies power the ecosystem.
❌ Application Layer
📊
Compete on features
Race to the bottom. Easy to copy.
🔄
Linear growth
Each customer = new acquisition cost
💰
2-5x revenue multiples
Commodity software pricing
🏃
Low switching costs
Customers can leave anytime
✓ Infrastructure Layer
🏗️
Compete on reliability
Mission-critical. Hard to replicate.
📈
Compound growth
Templates improve → more value → more customers
💎
10-25x revenue multiples
Scale AI: 18x. Stripe: higher.
🔒
High switching costs
Workflows built on your templates
Network effects are the underlying principle behind the success of companies like AWS, Stripe, and Salesforce. Higher network density means the product value increases.
— NFX: The Network Effects Manual
Market Size: $50-70B by 2030
AI agents are the fastest-growing category in enterprise software. We're building the infrastructure layer.
$7.8B
AI Agents Market (2025)
MarketsandMarkets
$52.6B
AI Agents Market (2030)
MarketsandMarkets
46.3%
CAGR Growth Rate
2025-2030 forecast
$183B
Bullish Forecast (2033)
Grand View Research
Our TAM Slice: Infrastructure
If AI Agents are $50B, infrastructure is 20-30% of stack value:
$10-15B
Agent Infrastructure TAM by 2030
Why We Win This Slice
🎯
First-mover on playbooks
Every month = more encoded knowledge
💰
Revenue while building
Services fund the platform
🧠
Real deployment data
Failure patterns competitors don't have
The Infrastructure Stack
Four layers that make AI agents reliable. We're building all four.
1
Workflow Templates
Verified prompts, sequences, integrations
Verified prompts, sequences, integrations
2
Agent Orchestration
Multi-agent coordination, task routing
Multi-agent coordination, task routing
3
Human Oversight
Approval queues, escalation, feedback loops
Approval queues, escalation, feedback loops
4
Guardrails + Observability
Safety rails, monitoring, audit trails
Safety rails, monitoring, audit trails
Current Products
🛡️
Agent Seatbelt
Browser-layer guardrails that block irreversible actions
📊
ClawView
Observability for autonomous agents. See what they do.
🏛️
AgentGov
Governance, compliance, audit trails
📚
AgentDocs
Verified code snippets for agent tool use
Current Traction
$4K
MRR
5
Paying Clients
3
Workflow Types
SDR, Video Gen, Research
+$2K
Added This Week
What We've Delivered
🏗️
SDR for construction companies
Lead gen + qualification workflows
🎬
Video generation for ML training
Synthetic data pipeline workflows
🔬
Research for universities
Literature review + synthesis workflows
🚀
BDR for startups
Outbound + meeting booking workflows
What This Proves
Fat Startup Thesis
We're getting paid to build our moat. Every dollar of revenue = more encoded knowledge. Competitors starting later start from zero.
"A fat startup ships outcomes, not features. It bundles software, data, and human ops into one integrated product that actually gets the job done."
— Andrew Lee, a16z Speedrun
The Path Forward
🛠️
Year 1: Services
$1M ARR · 50+ playbooks
📚
Year 2: Productize
Self-serve templates
🏗️
Year 3: Platform
Others build on us
12-Month Milestones
💰
$1M ARR
Prove unit economics at scale
📚
50+ Workflow Templates
Across 5+ verticals
🔧
Infrastructure Products Live
Guardrails, Observability, Governance
📦
First Self-Serve Templates
Deploy without our team
Why Now
🚀
Models just got capable enough
GPT-5, Claude 4—agents can work
💔
AI SDR market burned
70-80% churn = customers seeking alternatives
⏰
Infrastructure window open
No dominant player yet. First-mover wins.
📜
Regulatory tailwinds
EU AI Act mandates oversight, audit trails
The Ask
The AWS of AI Work
Infrastructure that makes AI agents reliable. Workflow templates. Orchestration. Human oversight.
Every company deploying agents will need this. We're building it.
Team
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed
Yasir
Co-Founder
yapthis.com · Shipped production agents
Key Sources
MIT NANDA Study: 95% AI failure rate, 171% ROI when successful
MarketsandMarkets: $7.8B → $52.6B AI agents market (2025-2030)
Scale AI (Sacra): $1.5B ARR, $29B valuation, 50% gross margins
Pilot (CNBC/TechCrunch): $1.2B valuation, Bezos-backed
11x/Artisan: 70-80% churn within months (Broadn research)
RAND Corporation: 80% AI project failure rate
MARKETPLACE THESIS
🚗 OpenHolly
The Uber for AI Work
Post an outcome. AI agents compete. Pay only for results. We're building the outcome marketplace for the AI economy.
$4K
MRR Today
70%
Network effects create tech value
NFX Research
$13.8B
Scale AI valuation
Services → Platform
$60M+
GitCoin distributed
Bounty model works
The a16z Speedrun Thesis
This is the exact model a16z partners are calling for in 2026.
Say you need 50 qualified sales meetings. Instead of buying another AI tool, you post a bounty: "$500 per meeting booked." AI agents compete. Whoever performs best gets paid. We already do this with bug bounties, Kaggle, hackathons. Why not for AI agents going after real business outcomes?
— Macy Mills, a16z Speedrun, "14 Big Ideas for 2026"
I'm especially excited about products that use AI to make previously expensive services cheaper and more accessible, sometimes using human-in-the-loop to start.
— Kenan Saleh, a16z Speedrun, "14 Big Ideas for 2026"
A fat startup ships outcomes, not features. It bundles software, data, and human ops into one integrated product that actually gets the job done.
— Andrew Lee, a16z Speedrun Partner
The Market Shift: Tools → Outcomes
The freelance marketplace is $1.5T. It's about to be disrupted by AI agents.
❌ Legacy Marketplaces
📝
Upwork: $1.67B market cap
Pay humans by the hour. Hope they deliver.
📝
Fiverr: ~$1B market cap
Fixed-price gigs. Still human-dependent.
🐢
Slow, expensive, variable
Wait days. Pay premium. Quality varies.
✓ AI Agent Marketplace (Us)
🎯
Pay per outcome, not effort
$X per meeting, $Y per video, $Z per lead.
⚡
Hours, not days
AI agents work 24/7. Instant scale.
📈
Network effects compound
More agents = better matching = better outcomes.
The Paradigm Shift
As we move to a future based on outcome-based pricing that perfectly aligns incentives between vendors and users, we'll first move away from time-based billing. — a16z Big Ideas 2026
How It Works
Bounties + Escrow + AI Agents = Outcome Marketplace
🎯
Post Bounty
"50 meetings @ $500 each"
💰
Escrow Funds
Payment locked
🤖
Agents Compete
Best performers win
✅
Verify & Release
QA passes → pay out
For Buyers
📝
Define the outcome
"Book qualified meeting" or "Generate product video"
💵
Set your price
Pay what the outcome is worth to you
🔒
Zero risk
Funds held in escrow. Pay only on delivery.
For Agents (Supply Side)
🎰
Pick bounties that fit
Match capabilities to opportunities
📊
Build reputation
Success rate → more bounties → more revenue
💰
Get paid instantly
Verified outcome → automatic payout
The Bounty Model Works
Proven in bug bounties, open source, and ML competitions. Now it's time for AI work.
$60M+
GitCoin distributed
Open source bounties
$100M+
Bug bounties/year
HackerOne + Bugcrowd
$1B+
Kaggle prize pool
ML competitions
10M+
Replit users
Bounties marketplace
Precedent: Replit Bounties
Imagine a tool where you describe your problem and get a solution built for you. Today we're introducing Bounties, a marketplace where you work with top creators and bring your software ideas to life.
— Replit, on launching Bounties
Replit proved bounties work for code. We're proving it works for any AI-deliverable outcome.
Precedent: GitCoin
Over the past 5 years we've supported the funding of public goods. Started with bounties for open source, evolved to quadratic funding.
— GitCoin: $60M+ distributed
GitCoin proved bounties + crypto payments = massive coordination. We're applying this to AI agent work.
Network Effects: The Moat
70% of tech value comes from network effects. Here's how we build them.
Network effects have been responsible for 70% of all the value created in technology since 1994. Founders who deeply understand how they work will be better positioned to build category-defining companies.
— NFX, "The Network Effects Bible"
Two-Sided Marketplace NFX
👤
More buyers → More bounties
Attracts more agents to the platform
🤖
More agents → Better matching
Faster delivery, higher quality outcomes
📈
Better outcomes → More buyers
Word of mouth, lower prices, faster delivery
Data Network Effects
📊
Every bounty = training data
What works, what fails, edge cases
🧠
Smarter matching over time
Route bounties to best-fit agents
🔒
Proprietary playbook library
Compound knowledge competitors can't replicate
Metcalfe's Law
Value of a network grows proportional to N² (nodes squared). With agents AND buyers, we get cross-side network effects that compound faster than single-sided platforms.
Trust Layer: How Agents Build Reputation
The missing infrastructure for AI agent marketplaces.
Agent Identity & Track Record
🆔
Verifiable agent identity
Who built it, what it can do, audit trail
📈
Per-function reputation
Track record based on actual outcomes, not reviews
🏆
Specialization scores
"This agent is 94% on sales meetings, 78% on video"
Trust Mechanics
🔒
Escrow with time-locks
Funds released only on verified delivery
⚖️
Dispute resolution
Human or AI arbitration for edge cases
📉
Sliding refund scale
Partial credit for partial delivery
🆕
New Agent
Low trust, small bounties
📊
Track Record
Outcomes verified
⭐
Trusted Agent
High-value bounties
🏅
Elite Status
Premium rates, priority
Path: Managed → Open Marketplace
Like Uber: start premium, then open the platform.
🛠️
Phase 1: Now
We run the agents
🤝
Phase 2: Partners
Vetted agent builders
🌐
Phase 3: Open
Any agent can join
Phase 1: Managed (Now)
✅
We operate all agents
Quality control, learn playbooks
✅
$4K MRR validates demand
Customers paying for outcomes
✅
Build trust infrastructure
Escrow, verification, reputation
Phase 2-3: Marketplace
🔜
Invite partner agents
Vetted builders, revenue share
🔜
Open to all agents
Anyone can compete for bounties
🔜
Platform take rate: 15-20%
Like Uber, Airbnb, marketplace standard
The Uber Playbook
Uber started with black cars (premium, managed) before opening to UberX (open marketplace). We start with our agents, prove economics, then open to all. Services fund the platform build.
Comparable Companies & Valuations
Services → Platform is a proven path to massive outcomes.
$13.8B
Scale AI
Data labeling services → platform
$1.67B
Upwork
Freelance marketplace (ripe for disruption)
$1.2B
Pilot
Bookkeeping: humans + AI
$50B+
Palantir
Services → Platform → Public
Scale AI: Our North Star
1️⃣
Started as services
Data labeling for ML companies
2️⃣
Built the platform
Tools, workflows, quality systems
3️⃣
$2B+ revenue (2025)
Services funded the infrastructure
4️⃣
$13.8B valuation
Platform economics, not services multiples
Why We're Bigger
📊
Scale AI: One vertical
Data labeling for ML
🌐
Us: All AI-deliverable work
Sales, content, research, ops...
📈
TAM: $1.5T+ services market
Every white-collar task that can be AI'd
Why Now: The Perfect Storm
GPT-5
Agents now capable
x402
Machine payments ready
a16z Big Ideas 2026
70%
AI SDR churn
Tools failing, outcomes wanted
$1B+
AI coding revenue (2025)
a16z: Agent apps thriving
Technology Inflection
🧠
Models capable enough
GPT-5, Claude 4 can do real work
💳
x402 machine payments
Agents can transact autonomously
🔧
Infrastructure exists
OpenClaw, MCP, agent frameworks
Market Readiness
💔
AI tools disappointing
70% churn = buyers want outcomes
💰
Budget exists
Companies spending on AI, getting nothing
🏃
First mover advantage
No AI-native outcome marketplace yet
Emerging primitives like x402 make payment settlement programmable and reactive. Smart contracts can settle a dollar payment globally in seconds. In 2026, this becomes the rails for agent commerce.
— a16z Big Ideas 2026, Part 3
Team & Traction
$4K
MRR
5
Customers
3-7x
Margin Multiple
0%
Churn (outcome-aligned)
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure experience
Yasir
Co-Founder
yapthis.com · Agentic architecture · Shipped production agent systems
What Traction Proves
Companies pay for outcomes. 0% churn because incentives align. This is the business model for AI work.
The Ask
What We Need
💰
$[X] Pre-Seed
Scale agent capacity, build marketplace infra
🎯
12-month goal: $1M ARR
Prove economics before opening marketplace
🌐
24-month: Open marketplace
Partner agents, then fully open
Why Us
🐕
Dog-fooding OpenClaw
We run agents daily, know what breaks
📊
Built the infrastructure
ClawView, guardrails, workflows
💵
Revenue already
$4K MRR proves the model
OpenHolly: The Uber for AI Work
Post an outcome. AI agents compete. Pay for results. The marketplace that makes AI actually deliver.
🔧 Infrastructure We're Building
🛡️ Guardrails • 📊 ClawView • 🏛️ AgentGov
Trust layer that makes marketplace outcomes reliable.
📚 Sources
a16z: "14 Big Ideas for 2026" (Macy Mills, Andrew Lee, Kenan Saleh) • "Big Ideas 2026 Part 1-3" • NFX: "The Network Effects Bible" (70% of tech value) • Market Data: Scale AI ($13.8B), Upwork ($1.67B), GitCoin ($60M+ distributed) • Replit: Bounties marketplace launch
1 / 12
CONTROL PLANE THESIS
🎛️ OpenHolly
The Control Plane for
AI Agents
Everyone's building autonomous agents. We're building the layer that makes them actually work: purpose-built infrastructure for human oversight at scale.
95%
AI pilots fail to deliver ROI
MIT Research, 2025
17x
Error amplification in "bag of agents"
DeepMind, Dec 2025
71%
Accuracy improvement with HITL
Microsoft Magentic-UI
$4.5K
MRR proving the thesis
OpenHolly, Feb 2026
2 / 12
The Inconvenient Truth: Autonomy Fails
The research is clear—and the industry is learning the hard way.
Multi-Agent Systems Break Down
"Multi-agent architectures, despite their promise, can fall short on efficiency, reliability, and even accuracy... performance often degrades as coordination complexity increases."
— Berkeley/DeepMind "Why Multi-Agent LLM Systems Fail", 2025
📊
75% failure rate
ChatDev on ProgramDev benchmark
📊
~50% average task completion
Across autonomous agent frameworks
📊
17x error amplification
In uncoordinated "bag of agents"
Enterprise AI Projects Crater
"42% of companies abandoned most of their AI initiatives in 2024, up from 17% the previous year. The average organization scrapped 46% of AI proof-of-concepts."
— S&P Global Research, 2024
📊
95% of AI pilots fail
MIT Research on enterprise deployments
📊
80%+ never reach production
RAND Corporation AI project study
📊
2x failure rate vs traditional IT
AI projects vs standard software
Why This Matters
The industry is betting billions on fully autonomous agents. The research says they don't work. Someone needs to build the layer that makes them work.
3 / 12
Microsoft's Answer: Human-in-the-Loop
The largest AI research org in the world just validated our thesis.
"We argue that human-in-the-loop agentic systems offer a promising path forward, combining human oversight and control with AI efficiency to unlock productivity from imperfect systems."
— Microsoft Research, Magentic-UI (July 2025)
Magentic-UI Results
71%
Accuracy improvement with human-in-loop
30.3% → 51.9% on GAIA benchmark
📊
Only 10% of tasks needed human help
Lightweight intervention, massive improvement
📊
1.1 avg clarifications per help request
Minimal interaction overhead
Key Interaction Mechanisms
🤝
Co-planning
Human + agent collaborate on plan before execution
🔄
Co-tasking
Seamless handoff between human and agent control
🛡️
Action guards
Human approval for high-stakes actions
🧠
Memory
Learn from past interactions to improve
Microsoft's Conclusion
"Even as tomorrow's agents become more capable and reliable, we believe that human involvement will remain essential for preserving human agency, resolving unforeseen ambiguities, and guiding agents in adapting to an ever-changing world."
4 / 12
Anthropic's Findings: The Oversight Paradox
Real-world data from millions of Claude Code sessions reveals how humans actually oversee agents.
As Users Gain Experience...
📈
Auto-approve increases: 20% → 40%+
Experienced users let Claude run autonomously
📈
BUT interrupt rate ALSO increases: 5% → 9%
They intervene more often, not less
💡
The shift: Step-by-step → Exception-based
From approving everything to watching for problems
Agent-Initiated Stops Matter
🤖
Claude asks for clarification 2x more
On complex tasks vs simple ones
🤖
More often than humans interrupt
On the most difficult tasks
💡
Models know when they're uncertain
They can (and should) ask for help
"Effective oversight doesn't require approving every action but being in a position to intervene when it matters... our central conclusion is that effective oversight of agents will require new forms of post-deployment monitoring infrastructure and new human-AI interaction paradigms."
— Anthropic Research, "Measuring AI Agent Autonomy in Practice" (Feb 2026)
The Deployment Overhang
Anthropic found that "the autonomy models are capable of handling exceeds what they exercise in practice." The bottleneck isn't model capability—it's the oversight infrastructure.
5 / 12
Air Traffic Control for AI Agents
The analogy everyone is converging on—and what it means for product design.
"Think of agents within your multi-agent system as the airplanes. The agents have their own autonomy to act. But air traffic control provides guardrails, coordination, and human oversight for the whole system."
— Jason Bryant, AI in Pharma (Jan 2026)
Why Air Traffic Control Works
✈️
Planes are autonomous
Pilots make real-time decisions
🗼
Controllers handle coordination
Routing, conflicts, emergencies
👤
Humans handle edge cases
Technology can't modify standard procedures
🔄
System improves over time
Incidents become new procedures
Why This Analogy Matters
📊
Scaling ratio: 1 controller : many planes
Not 1:1 human-to-agent
🛡️
Controllers can't replace pilots
Nor vice versa—complementary roles
⚠️
No full automation possible
Edge cases require human judgment
💰
Multi-billion dollar industry
ATC isn't going away
The Thesis
As AI agents proliferate, every company will need an "air traffic control" system for their agent fleet. That's the control plane we're building.
6 / 12
Why Current Interfaces Fail
Existing tools weren't designed for the human-agent oversight problem.
❌ Chat Interfaces
Conversational, not workflow-oriented. Can't manage 100 agents. No approval queues. No batch operations. You'd need a chat window per agent.
❌ Code/GitHub
Great for developers. Useless for ops teams. Can't approve actions in real-time. No visual understanding of agent state or intent.
❌ Slack/Email Alerts
Ad hoc approvals. No context. Alert fatigue. Doesn't learn from decisions. Can't see what agent plans to do next.
❌ Observability Dashboards
Read-only visibility. No intervention capability. See problems after they happen. Can't modify agent plans mid-execution.
"Only 14.4% of enterprises have full security approval for AI agents. 88% reported agent-related incidents. The interface problem is also a governance problem."
— Gravitee State of AI Agents Report, 2026
The Gap
There's no purpose-built interface for humans to oversee AI agents at scale. Not dashboards. Not chat. Not alerts. A new category needs to exist.
7 / 12
What a Control Plane Actually Needs
Distilled from Microsoft, Anthropic research, and our own deployments.
Pre-Execution
📋
Plan Review
See what agent intends to do before it acts. Edit plans. Add constraints.
🎯
Scope Boundaries
Define allowed domains, tools, actions. Agent can't exceed boundaries.
🔗
Workflow Templates
Start from proven patterns. Don't reinvent for every task.
During Execution
👁️
Real-Time Visibility
See agent actions as they happen. Browser view. Code execution. API calls.
⏸️
Interrupt & Resume
Pause any agent instantly. Take control. Hand back.
🛡️
Action Guards
Automatic pause for high-stakes actions. Configurable thresholds.
Approval Layer
📥
Unified Queue
All pending approvals across all agents in one view.
🎛️
Batch Operations
Approve/reject patterns across many agents at once.
🔀
Smart Routing
Route different decisions to different humans by expertise.
Learning Layer
🧠
Decision Memory
Human approvals become future patterns. Rejections become rules.
📈
Threshold Tuning
Auto-adjust when to ask humans based on outcomes.
📚
Playbook Evolution
Workflows improve with every human intervention.
8 / 12
The "Control Plane" Category
Every complex system has a control plane. AI agents need one too.
🐕
Datadog
$50B+ market cap
Control plane for infrastructure. See what's happening. Alert when things break. Intervene.
☸️
Kubernetes
Industry standard
Control plane for containers. Orchestrate workloads. Handle failures. Scale automatically.
🔐
Okta
$15B+ market cap
Control plane for identity. Who can access what. Audit trails. Compliance.
🎛️
???
AI Agent Control Plane
What agents are doing. Approvals & intervention. Learning & guardrails. This category doesn't exist yet.
"The control plane provides management and orchestration across an organization's environment. It's akin to air traffic control for applications."
— Vectra AI definition
The Opportunity
Infrastructure got Datadog. Containers got Kubernetes. Identity got Okta. AI agents need their control plane. We're building it.
9 / 12
Why Human-in-the-Loop Scales
The VC objection—and why it's wrong.
The Objection
"If humans are in the loop, doesn't that kill unit economics? Isn't the whole point to remove humans?"
The Response: Look at the Data
Scale AI
$13.8B valuation
Human labelers + AI. Humans as oversight.
Pilot
$1.2B valuation
Human bookkeepers + AI. Humans as QA.
Palantir
$50B+ market cap
Human analysts + AI. Humans as strategists.
The Key Distinction
"Humans as OVERSIGHT, not labor. AI does the work, humans QA. The ratio improves over time."
The Scaling Math
1️⃣
Year 1: 10:1 ratio
1 human oversees 10 agents. Heavy QA.
2️⃣
Year 2: 100:1 ratio
System learns. Fewer interventions needed.
3️⃣
Year 3+: 1000:1 ratio
Humans handle edge cases only. Still critical.
The Avi Medical Case Study
81% automation rate. 93% cost savings. Humans handle complex cases. HITL doesn't kill unit economics—it enables them.
10 / 12
The Contrarian Bet
Everyone's zigging toward full autonomy. We're zagging toward control.
What Everyone Else is Building
🤖
Fully autonomous agents
Demo well. Break in production.
🤖
More agent capabilities
Better models. More tools. Same failure modes.
🤖
"Just add more agents"
17x error amplification, per DeepMind.
🤖
Removing humans entirely
The dream that keeps failing.
What We're Building
🎛️
The oversight layer
Makes ANY agent more reliable.
🎛️
Human-agent collaboration
Complementary strengths. Better outcomes.
🎛️
Coordination infrastructure
Turns bag-of-agents into functional team.
🎛️
Humans in the right places
Exception handling. Strategic oversight.
"I'm especially excited about products that use AI to make previously expensive services cheaper and more accessible, sometimes using human-in-the-loop to start."
— Keenan Saleh, a16z Speedrun Partner
Our Position
We're not betting against agent capabilities improving. We're betting that oversight infrastructure will always be needed—and no one is building it well.
11 / 12
Why Us, Why Now
Team
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure experience
Yasir
Co-Founder
yapthis.com · Agentic architecture · Shipped production agent systems
Why Now
📈
Agent adoption is exploding
OpenAI Operator, Anthropic Claude Code, 1000+ agent startups
💔
Failure rates are becoming visible
95% pilot failure is now common knowledge
📄
Research is converging
Microsoft, Anthropic, DeepMind all pointing to HITL
🏛️
Regulation is coming
EU AI Act mandates audit trails & oversight
What We've Built
✅
$4.5K MRR
Proving the thesis with real customers
✅
OpenClaw infrastructure
Dogfooding our own control plane daily
✅
Guardrails, ClawView, AgentGov
Components of the full control plane
12 / 12
The Ask
The Human-Agent Control Plane
Purpose-built infrastructure for human oversight of AI agents at scale. Plan review. Action guards. Approval queues. Learning loops. The missing layer that makes agents actually work.
What We Need
💰
$[X] Pre-Seed
Build the full control plane product
🎯
12-month goal: $1M ARR
Prove control plane scales across customers
📚
Then: Category definition
Be "Datadog for AI agents"
The Opportunity
📈
New category creation
No one owns "AI agent control plane" yet
📈
Research-backed thesis
Microsoft, Anthropic, DeepMind alignment
📈
Every agent deployment needs this
Horizontal opportunity across industries
🔧 Infrastructure We're Building
🛡️ Guardrails • 📊 ClawView • 🏛️ AgentGov • 🤖 Employee OS
The Control Plane integrates all infrastructure layers into one human-facing interface.
🔬 Research Foundation
MIT: 95% of AI pilots fail · DeepMind: 17x error amplification in multi-agent · Microsoft Magentic-UI: 71% accuracy improvement with HITL · Anthropic: "New oversight infrastructure needed" · Berkeley: "Why Do Multi-Agent Systems Fail?" · S&P Global: 42% of AI initiatives abandoned
1 / 12
VIBE CODING OUTCOMES
✨ OpenHolly
Vibe Code Your Business
"Vibe coding" revolutionized app development—describe what you want, AI builds it. Now apply this to business outcomes. Describe the result, AI + humans deliver it.
Feb 2025
Karpathy coins "vibe coding"
X/Twitter
2026
"Vibe productivity" emerges
Beyond just coding
71%
Accuracy boost with HITL
Microsoft Magentic-UI
$4K
MRR proving the thesis
2 / 12
The Vibe Coding Revolution
What started as a meme became a paradigm shift. Now it's evolving beyond code.
"There's a new kind of coding I call 'vibe coding', where you fully give in to the vibes, embrace exponentials, and forget that the code even exists."
— Andrej Karpathy, Feb 2025 (coined the term)
Origins & Evolution
2023
"The hottest new programming language is English"
Karpathy's early prediction about LLM capabilities
2025
Vibe coding goes mainstream
Cursor, Replit, Claude Code—describe → build
2026
Beyond coding: "Vibe Productivity"
Research, writing, reporting, file operations, "glue work"
Where It's Going
"What changed in early 2026 is that vibe coding is no longer confined to software development; it is spreading into research, writing, reporting, spreadsheet wrangling, file operations, and 'glue work' that usually fragments attention."
— Ken Huang, "The Vibe Shift" (Jan 2026)
The Pattern
Vibe coding showed that natural language → complex software works. Now we're applying the same pattern to natural language → business outcomes.
3 / 12
From Apps to Outcomes
The next evolution: describe what you want to achieve, not what you want built.
💻
Vibe Coding
"Build me an app that..."
✨
Vibe Outcomes
"Get me 50 sales meetings"
🎯
Result
Meetings on your calendar
❌ Current Reality: Use Tools
1
Subscribe to AI SDR tool
$5-10K/month
2
Configure the tool
Import lists, write sequences, set rules
3
Monitor the tool
Fix errors, adjust settings, babysit
4
Hope for outcomes
70% churn in 3 months when it doesn't work
✓ Vibe Outcomes: Describe Results
1
Describe what you want
"50 qualified sales meetings with Series A fintech founders"
2
AI agents execute
Research, outreach, qualification, scheduling
3
Humans QA
Review, approve, handle edge cases
4
Pay for outcomes
$X per meeting delivered
The Thesis
Vibe coding proved that intent → artifact works for software. Vibe outcomes proves it works for business results. The "vibes" are the goal—the execution is handled by well-orchestrated HITL agent workflows.
4 / 12
How It Works
Describe outcome → Agents execute → Humans QA → Outcome delivered
💬
Natural Language
"I need..."
📋
Workflow Generation
Map to playbook
🤖
Agent Execution
Multi-agent work
👤
Human QA
Review & approve
✅
Outcome
Delivered
Example: "50 Sales Meetings"
1
Input
"Book 50 qualified meetings with Series A fintech founders in Q1"
2
Research Agent
Identifies prospects, signals, contact info
3
Outreach Agent
Drafts personalized messages
4
Human Review
Approves messaging before send
5
Scheduling Agent
Books the meeting when prospect replies
Example: "Process These Invoices"
1
Input
"Process this month's invoices and flag anomalies"
2
Extraction Agent
Pulls data from PDFs, emails, systems
3
Matching Agent
Matches to POs, identifies discrepancies
4
Human Review
Approves exceptions, flags fraud
5
Output
Processed invoices, exception report
5 / 12
Why Vibe Outcomes Need Human-in-the-Loop
Pure AI can't deliver reliable business outcomes. The research is clear.
95%
AI pilots fail to deliver ROI
MIT NANDA Study
30%
Lower success when agents collaborate
CooperBench, 2026
38.9%
Cite accuracy as #1 AI challenge
Industry analysts, 2025
71%
Accuracy improvement with HITL
Microsoft Magentic-UI
Why Pure AI Fails
"Multi-agent architectures, despite their promise, can fall short on efficiency, reliability, and even accuracy... performance often degrades as coordination complexity increases."
— Berkeley/DeepMind, 2025
⚠️
Hallucinations occur even with high confidence
AI can be confidently wrong about business-critical decisions
⚠️
Edge cases are infinite
Business has nuance AI can't anticipate
⚠️
Stakes are high
Brand damage, legal liability, lost deals
Why HITL Fixes It
"Hybrid AI workflows, which combine automation with human oversight, are not a fallback; they're the modern standard for reliability, trust, and scalability in 2026."
— Parseur, Dec 2025
✓
Human as QA layer, not labor
AI does 90% of work, humans verify critical decisions
✓
Trust calibration over time
System learns when to ask, when to proceed
✓
Only 10% of tasks need human help
Microsoft found lightweight intervention = massive improvement
6 / 12
The Interaction Layer
This is the UX for the AI-native agency, control plane, and marketplace pitches.
Why Current Interfaces Fail
❌ Chat Interfaces
Conversational, not outcome-oriented. Can't manage complex multi-step workflows. No approval queues.
❌ Dashboards
Read-only visibility. No intervention. See problems after they happen. Can't modify plans mid-execution.
❌ Slack/Email Alerts
Ad hoc. No context. Alert fatigue. Can't see what agent plans to do next.
The Vibe Outcomes Interface
💬
Natural language input
"I need X" → system figures out how
📋
Progress visibility
See what's happening toward your goal
🎛️
Approval queues
Review decisions that matter
⏸️
Interrupt & adjust
Course-correct mid-execution
📊
Outcome tracking
Clear metrics: delivered vs requested
🔗 This Powers Our Other Pitches
⚡ Fat Startup: Vibe outcomes is how customers interact with us
🚗 Uber for AI Work: Natural language bounty posting
🎛️ Control Plane: The human oversight layer
☁️ AWS of AI Work: Workflow templates activated by intent
7 / 12
Market Opportunity
The shift from "tools" to "outcomes" is creating massive new markets.
$52.6B
AI Agents Market by 2030
MarketsandMarkets
30%+
Enterprise SaaS with outcome-based pricing
Gartner 2025 Projection
61%
CFOs changing how they evaluate AI ROI
Industry Survey, 2025
$1.5T
Global professional services (TAM)
Work that can be "vibe coded"
Who Wants This
🏢
SMBs frustrated with AI tools
70% AI SDR churn = customers seeking alternatives
🏢
Enterprises with AI fatigue
95% pilot failure = demand for what works
🏢
Founders too busy to manage AI
Want outcomes, not another tool to learn
The Pricing Shift
"Per-seat is no longer the atomic unit of software. When AI can handle ticket resolution, the natural pricing metric becomes successful outcomes."
— a16z Enterprise Newsletter, Dec 2024
💰
Outcome-aligned pricing
$X per meeting, $Y per processed invoice, $Z per video
8 / 12
Competitive Landscape
Who else is thinking about natural language → outcomes?
Tools (Not Outcomes)
AI SDRs (11x, Artisan, AiSDR)
Sell tools. Charge per seat. You manage agents. 70% churn.
❌ Not outcome-based
Agent Platforms (LangChain, CrewAI)
Infrastructure for developers. Build your own workflows.
❌ Not outcomes, just primitives
Automation (Zapier, Make)
Workflow automation. You design the flows.
❌ Not AI-native, not outcome-based
Closest Parallels
Scale AI ($13.8B)
Services + HITL → platform. "We need labeled data" → delivered.
✓ Outcome-based, HITL model
Pilot ($1.2B)
"Do my bookkeeping" → done. Humans + AI.
✓ Outcome-based, HITL model
Intercom Fin ($0.99/resolution)
AI support priced per successful outcome.
✓ Outcome-based pricing model
Our Differentiation
Horizontal, not vertical. Scale AI = data labeling. Pilot = bookkeeping. We're building the general-purpose vibe outcomes platform—natural language to any deliverable business result.
9 / 12
Current Traction
Proving the thesis with real customers and real outcomes.
$4K
MRR
5
Customers
0%
Churn
3
Outcome Types
Outcomes We've Delivered
📅
"Get me sales meetings"
SDR/BDR for construction, startups (50% of revenue)
🎬
"Generate training videos"
ML training data pipelines (30% of revenue)
📚
"Research these topics"
University lab literature synthesis (20% of revenue)
Why Zero Churn
"When you only pay for outcomes, there's no reason to churn. We deliver meetings, they pay. We don't deliver, they don't pay. Aligned incentives = sticky customers."
vs. AI Tool Churn
AI SDRs charge $5-10K/mo whether or not they work. When they don't deliver, customers leave. Misaligned incentives = 70% churn.
10 / 12
Why Now: 2026 Is the Year
Technology, market, and cultural convergence make this the moment.
Technology Ready
🧠
Models finally capable enough
GPT-5, Claude 4 can execute real business workflows
🔧
Agent infrastructure exists
OpenClaw, MCP, tool-use protocols
💳
x402 machine payments
Agents can transact autonomously (a16z Big Ideas 2026)
📊
HITL research converging
Microsoft, Anthropic, DeepMind all pointing same direction
Market Ready
💔
AI tool fatigue
70% AI SDR churn. 95% pilot failure. Customers want what works.
💰
Budget exists
Companies spending billions on AI, getting nothing
📈
Pricing shift happening
30%+ enterprise SaaS moving to outcome-based
🎯
"Vibe coding" cultural moment
Natural language → results is now understood
"2025 was widely labeled 'the year of AI agents.' In reality, it was the year we learned what agents can and cannot do. 2026 is the year we build systems that work reliably, repeatedly, and in production."
— Human-in-the-Loop Newsletter, Dec 2025
11 / 12
Team
Keith Schacht
Co-Founder + Advisor
$140M exit (Mystery) · a16z funded · Built consumer products used by millions
[CTO]
Co-Founder & CTO
CTO of Because · $3M Seed · Deep agent infrastructure experience
Yasir
Co-Founder
yapthis.com · Agentic architecture · Shipped production agent systems
What We've Built
🐕
Dog-fooding daily
Running OpenClaw infrastructure ourselves
🛡️
Agent Seatbelt
Browser-layer guardrails
📊
ClawView
Agent observability
📚
Workflow templates
Playbooks that compound
Why Us
✓
We've shipped outcomes
$4K MRR from real deliverables
✓
We understand HITL
Built the infrastructure, not just the agents
✓
We know the failure modes
Encoded in playbooks from real experience
12 / 12
The Ask
Vibe Code Your Business
Describe the outcome you want. AI agents + human QA deliver it. Pay only for results. The interaction layer for the AI economy.
What We Need
💰
$[X] Pre-Seed
Scale agent capacity + build the interface
🎯
12-month goal: $1M ARR
Prove vibe outcomes across multiple verticals
📦
Then: Self-serve platform
Anyone can describe outcomes and get them
The Opportunity
📈
New category creation
"Vibe outcomes" platform doesn't exist yet
📈
Cultural moment
Vibe coding is mainstream—extend it to business
📈
$52.6B market by 2030
AI agents + outcome-based pricing converging
📚 Research Foundation
Karpathy: Coined "vibe coding" Feb 2025 · RAND: 80% AI project failure · Microsoft Magentic-UI: 71% accuracy improvement with HITL · CooperBench: 30% lower success in multi-agent without coordination · a16z: Outcome-based pricing shift · Gartner: 30%+ enterprise SaaS with outcome pricing by 2025 · Bessemer: AI Pricing Playbook (Feb 2026)
🔗 Related Pitches
⚡ Fat Startup • 💰 Outcome-Based • 🚗 Uber for AI Work • 🎛️ Control Plane
Vibe Coding Outcomes is the UX/interaction layer that powers all of these.
Research • NYC Target Companies
01
25 NYC Startups: R&D Opportunities
Series A-B companies ($13M-$160M raised) with specific research they could implement but haven't.
25
NYC Tech Startups
$850M+
Combined Funding
75+
Research Opportunities
02
🏦 Fintech / Finance AI
Rogo — $75M (Series B)
Building "Wall Street's first AI analyst" — LLMs for financial reasoning
R&D Opportunities:
- Chain-of-Table reasoning — 40% more accurate on tabular financial data
- FinGPT fine-tuning — Open-source financial LLM for domain reasoning
- Toolformer for financial APIs — Teach LLMs to call Bloomberg/Reuters autonomously
Hook: "Your financial reasoning models could be 40% more accurate on tabular data with Chain-of-Table"
Farsight — $16M (Series A)
AI for finance — valuation models, deal analysis, Excel/PPT generation
R&D Opportunities:
- SpreadsheetLLM — Microsoft's approach to better spreadsheet understanding
- DocPrompting — Generate accurate documents with citations
- Table-GPT — Unified table understanding and generation
Hook: "SpreadsheetLLM could cut your Excel generation errors by 30%"
Aiera — $25M (Series B)
GenAI for financial professionals — broker research, earnings calls, filings
R&D Opportunities:
- LongLoRA — Process 10x longer earnings calls without quality loss
- RAG-Fusion — Multiple query generation for better retrieval
- Time-LLM — Repurpose LLMs for time series forecasting
Hook: "LongLoRA could let you process 10x longer earnings calls without quality loss"
Carbon Arc — $56M (Series A)
Marketplace for curated AI-ready datasets (Insights Exchange)
R&D Opportunities:
- Data-Juicer — Open-source data quality toolkit for AI datasets
- DataComp — Benchmark for dataset curation quality
- Synthetic data detection — Verify dataset authenticity/quality
Hook: "DataComp benchmarking could become your quality certification"
03
🏥 HealthTech / BioTech
Ataraxis — $20M (Series A)
AI for cancer precision medicine — analyzes data to identify optimal treatments
R&D Opportunities:
- CancerGPT — Few-shot learning for drug pair synergy prediction
- DrugCLIP — Contrastive learning for drug-target interaction
- Med-PaLM 2 — Google's medical LLM achieving expert-level performance
Hook: "CancerGPT's few-shot approach could expand your drug combination predictions 5x faster"
Inspiren — $35M (Series A)
AI + IoT for senior care — AUGi device for fall detection and patient monitoring
R&D Opportunities:
- RT-DETR — Real-time detection faster than YOLO
- Action recognition transformers — Video transformers for activity recognition
- Privacy-preserving pose estimation — On-device processing without cloud
Hook: "RT-DETR could cut your fall detection latency by 40% while running entirely on-device"
Slingshot AI — $40M (Series A)
AI for mental health — "Ash" chatbot simulates therapist-like conversations
R&D Opportunities:
- Constitutional AI for safety — Anthropic's approach to helpful + harmless
- EmoBERTa — Emotion-aware language model fine-tuning
- CBT dialogue systems — Structured therapeutic conversation flows
Hook: "Constitutional AI could reduce harmful responses by 80% while maintaining therapeutic value"
Camber — $30M (Series B)
Healthcare payment automation — streamlines insurance reimbursement
R&D Opportunities:
- Medical coding LLMs — Auto-coding diagnosis/procedure codes
- Claims denial prediction — ML to predict and prevent rejections
- Donut/Pix2Struct — Document understanding for medical forms
Hook: "Medical coding LLMs could auto-fill 60% of your claims forms"
04
🛠️ Dev Tools / Infrastructure
Warp — $18M (Series A)
AI-powered payroll platform for multi-state compliance
R&D Opportunities:
- Regulatory RAG — Retrieval over tax code databases
- LayoutLMv3 — Extract state tax forms with 95% accuracy
- Temporal reasoning — LLMs for date/deadline calculations
NetBox Labs — $35M (Series B)
Open-source network automation platform
R&D Opportunities:
- LLM for network config — Auto-generate Cisco/Juniper configs from NL
- Anomaly detection — Transformer-based time series for network telemetry
- Vision → IaC — Convert network diagrams to code
Topline Pro — $27M (Series B)
AI marketing for home service businesses
R&D Opportunities:
- Local SEO automation — LLM-generated location-specific content
- Multi-modal review response — Personalized responses with images
- Conversational scheduling — LLM-powered booking agents
05
💼 Sales / Marketing AI
Clay — $40M (Series B, $1.25B valuation)
AI for sales personalization — integrates 100+ data sources
R&D Opportunities:
- Persona-based email generation — LLMs that adapt tone per recipient
- Entity resolution at scale — Deduplication across data sources
- Buyer intent prediction — Multi-signal ML for ready-to-buy leads
Hook: "Buyer intent prediction could 3x your users' reply rates"
Profound — $35M (Series B) ⭐ Existing Client
AI search optimization — helps brands appear in AI-generated responses
R&D Opportunities:
- Retrieval optimization — Improve citation likelihood in RAG systems
- AI visibility benchmarking — Measure brand presence across LLMs
- Source authority scoring — How LLMs weight different sources
ShopMy — $77.5M (Series B)
Influencer commerce platform
R&D Opportunities:
- CLIP-based product matching — Visual similar product discovery
- Influencer-audience fit — ML for matching creators with brands
- Shoppable video AI — Auto-detect and tag products in video
06
⚖️ Compliance / Legal AI
Norm AI — $48M (Series B)
AI for regulatory compliance — automates review of legal documents
R&D Opportunities:
- Legal-BERT fine-tuning — Domain-specific transformer for legal text
- Contract element extraction — NER for legal clauses
- Regulatory change detection — Track and summarize regulation updates
Hebbia — $130M (Series B, $700M valuation)
Document AI — searches large document sets with citations
R&D Opportunities:
- ColBERT v2 — Late interaction retrieval for better search
- Self-RAG — LLM that self-reflects on retrieval quality (+25% accuracy)
- Structured reasoning chains — Better citation generation
Hook: "Self-RAG could improve your citation accuracy by 25%"
07
🔒 Cybersecurity / 🌱 Climate / 🛒 Consumer
Zip Security — $13.5M
SMB cybersecurity
- LLM threat intelligence
- Automated SOC analyst
- LLM phishing detection (+40% accuracy)
Chestnut Carbon — $160M
Reforestation + carbon credits
- Satellite carbon estimation
- Biodiversity monitoring (audio/visual)
- ML credit verification
GDI — $20M+
Silicon anodes for EV batteries
- Battery degradation prediction
- Materials discovery with ML
- CV defect detection (-40% QC cost)
Novig — $18M
P2P sports betting
- LLM odds modeling
- Market making algorithms
- Fraud detection
David — $75M
High-protein nutrition bars
- AI food formulation
- Demand forecasting
- Consumer preference modeling
Cents — $40M
Laundry/dry-cleaning SaaS
- Demand forecasting
- Route optimization
- Image garment classification
08
🎯 Best Targets by Category
🔥 Highest Urgency (AI-Native)
- Rogo — Financial reasoning is hard, need every edge
- Hebbia — Document AI is competitive, Self-RAG matters
- Aiera — Long context + time series = big opportunities
- Slingshot AI — Safety is existential for mental health AI
💰 Big Companies With Resources
- Clay ($1.25B val) — Can afford to experiment
- Hebbia ($700M val) — Research-forward culture
- Chestnut Carbon ($160M) — ML for verification is huge
🎯 Underserved Markets
- Inspiren — Elder care + CV is niche
- Cents — Laundry tech has zero AI competition
- Topline Pro — Home services AI is wide open
⭐ Existing Relationship
- Profound — Already a client, easy expansion
Outreach Template
Subject: Quick R&D idea for [Company] — [specific technique]
Hi [Name],
Congrats on [recent news/funding]. I've been researching [specific paper/technique] that could help with [their specific problem].
Quick version: [1-sentence benefit with number]
I put together a 2-page brief showing how this could work for [Company]. Want me to send it over?
Research • Positioning Analysis
01
R&D ≠ The Pain Point
The real market pain is downstream from R&D — it's about shipping AI to production.
80%
AI projects fail to reach production (RAND)
95%
GenAI pilots failing (MIT/Fortune 2025)
The gap isn't finding the right model. It's shipping AI to production.
02
The Skills Gap (Reddit Gold)
From r/MLQuestions — 688 upvotes, Nov 2025
What Candidates Know
- Transformer architectures, attention mechanisms
- Papers they've implemented (diffusion, GANs, LLMs)
- Kaggle competitions, theoretical deep learning
What Companies Need
- Deploy a model behind an API that doesn't fall over
- Write data pipelines that process reliably
- Debug why the model is slow/expensive in production
- Build evals to know if the model is working
"I'll interview someone who can explain LoRA fine-tuning in detail but has never deployed anything beyond a Jupyter notebook."
— Startup co-founder hiring ML engineers
03
The Observability Gap (Your Opportunity)
From Cleanlab's survey of 95 teams with AI in production
<1/3
Teams satisfied with observability
63%
Plan to improve observability next year
70%
Rebuild AI stack every 3 months
Key Insight
Even among the 5% of companies that reach production, most remain early in maturity. They can't reliably know when their agents are right, wrong, or uncertain.
04
Reframing The Pitch
| ❌ OLD: "AI R&D Engineer" | ✅ NEW: "Production AI Engineer" | |
|---|---|---|
| Vibes | Research, experimentation | Deployment, reliability |
| Perception | Nice-to-have | Need-to-have |
| Target | Teams with resources | Teams with stuck projects |
| Job-to-be-done | "Find the best model" | "Ship to production this month" |
The Positioning Gap
Aemon = the optimization engine
You = the shipping engine
05
Target Customers (Not Research Teams)
🚀 Series A-C Startups with AI Features
- Have small ML teams, can't hire fast enough
- ML engineers cost $200-400k and are hard to find
- Need someone who can actually deploy, not just research
Pain: "We have 3 AI features in Jira blocked for months"
🏢 Product Companies Adding AI
- Non-ML companies adding AI features
- Don't have ML expertise internally
Pain: "We want AI in our product but don't know where to start"
⚙️ Enterprise AI Platform Teams
- Drowning in stack churn (rebuilding every 3 months)
- Coordination overhead killing velocity
Pain: "Platform team of 5 supporting 20 feature teams — we're bottlenecked"
🏛️ Regulated Industries
- 42% plan to add oversight features (vs 16% unregulated)
- Need governance + observability
Pain: "Can't deploy AI without compliance sign-off"
06
Better Pitch Angles
1. "Your AI Projects Are Stuck. We Ship Them."
- Target: Companies with AI projects "in progress" for months
- Proof: Show deployment timelines (weeks vs months)
- Wedge: Audit → identify stuck projects → ship one fast
2. "AI Observability + Ops as a Service"
- Target: Companies with AI in production but no visibility
- Pain: "We don't know when our AI is wrong"
- Proof: Catch regressions, reduce incidents
3. "The AI Platform Team You Can't Hire"
- Target: Scaling startups without MLOps expertise
- Pain: ML engineers cost $400k and don't want to do ops
- Proof: Infrastructure setup in days, not months
4. "CI/CD for AI" (existing pitch)
- Still good, but position as production not research
- Focus on deployment gates, not model selection
- "Every AI PR tested against your evals before merge"
Action Items
- Rewrite pitches with "production" and "ship" language
- Target stuck projects — companies with AI features in backlog
- Lead with observability — 63% want better visibility
- Offer quick wins — "Ship one AI feature in 2 weeks"
- Avoid research teams — they don't have budget urgency
Research • AgentDocs Wedges
01
AgentDocs Wedges
AgentDocs Wedges
& Approaches
Based on Garry Tan's YC video insight: agents pick tools based on doc quality, not actual performance. The Whisper/Groq problem.
Claude Code defaulted to Whisper V1 — a near-deprecated model — because it has better documentation than Groq, even though Groq is 200x faster and 10x cheaper.
— Garry Tan, YC Partner, Feb 2026
The Insight
Agents pick tools based on doc quality, not actual performance — and that's exactly the gap AgentDocs exploits.
02
Wedge Scoring (6 Dimensions)
| Wedge | Mkt | Pain | Comp | Fit | x402 | Time | Total |
|---|---|---|---|---|---|---|---|
| 🥇 LLM / Model Routing | 5 | 5 | 3 | 5 | 4 | 5 | 27/30 |
| 🥈 Video Gen | 5 | 5 | 3 | 5 | 5 | 4 | 27/30 |
| 🥉 Audio / Transcription | 3 | 5 | 2 | 4 | 4 | 5 | 23/30 |
| Deployment / Hosting | 5 | 4 | 4 | 4 | 3 | 4 | 24/30 |
| Agent Identity (email/phone) | 4 | 5 | 4 | 3 | 3 | 5 | 24/30 |
| Databases | 5 | 4 | 3 | 3 | 2 | 4 | 21/30 |
| Image Gen | 4 | 3 | 5 | 3 | 4 | 3 | 22/30 |
03
x402 Market Reality Check
What agents are ACTUALLY spending on today (x402scan.com, Feb 2026)
$101K
24h Volume
513
Active Merchants
692
Crypto/Onchain Servers
0
Transcription Services
What Exists (Validated)
✓
Crypto/Onchain
692 servers — dominant vertical
✓
AI Servers
486 servers — led by Virtuals ACP ($163K/day)
✓
Search/Data APIs
216 servers — StableEnrich, httpay
✓
Trading Intelligence
203 servers — alpha signals
What's Missing (Opportunity)
0
Transcription
Zero servers — Garry Tan example!
~1
Video/Image Gen
42 txns — essentially nothing
0
Deployment/Hosting
Nothing
0
Databases
Nothing
04
Re-scored: x402 Demand vs Fit
| Wedge | x402 Now | Holly Fit | Verdict |
|---|---|---|---|
| Multi-API aggregation + capability layer | ✓ 3 players, no AgentDocs | ✓ Direct fit | Best immediate wedge |
| Agent-to-agent coordination | ✓ $163K/day (Virtuals) | ✓ Holly as orchestrator | Most validated demand |
| Social data for agents | ✓ StableSocial live | ✓ Fits Wurk agents | Niche but real |
| Transcription (Whisper/Groq) | ❌ Zero on x402 | ✓ Strong routing layer | 6–12 months early |
| Video gen | ❌ Near zero | ✓ Strong dogfood | 12–18 months early |
Key Insight
Absence of transcription/video/deployment on x402scan is opportunity signal, not rejection. StableEnrich proved the model: wrap existing APIs behind x402, get thousands of transactions immediately.
05
Recommended Launch Order
🎙️ 1. Transcription — NOW
Zero servers on x402. Garry Tan moment 6 days ago. First-mover window open.
AgentDocs value: Verified schema {input, model: "groq|deepgram|whisper", output}
Groq at $0.02/min → charge $0.03/min
"Your agent would have chosen Whisper V1. Ours chose Groq."
🎬 2. Video Gen — Dogfood Now
Parameter chaos problem (Kling uses cfg_guidance, Runway uses guidance_scale). Genuinely unsolved.
AgentDocs value: Agent sends {prompt, style, duration, budget}, Holly resolves params
Already dogfood — Holly generates video
🧠 3. LLM Routing — Big Vision
Agent says {task: "transcribe", latency: "fast"} → gets best provider with pricing + ready API call.
The purest AgentDocs wedge
📧 4. Agent Identity — HOT
Garry Tan: "Has anybody built Twilio for agents yet?"
Email + phone + wallet in one API call
Jared Friedman (YC): "Even the best dev tools don't let you sign up via API. This is a big miss in the claude code age — claude can't sign up on its own."
06
The Gap: Verified Snippet Services
3
Multi-API aggregators live
StableEnrich, httpay, LowPaymentFee
4.7K
Txns for StableEnrich
0
With capability contracts
0
With AgentDocs semantics
What They Do
Aggregate APIs (Apollo + Firecrawl + Grok + Serper) behind one x402 endpoint.
"Throw money at endpoint, get data back"
What They Don't Do
❌
Structured capability contracts
❌
Machine-readable reasoning
❌
Verification + benchmarks
The Unified Pitch
OpenHolly becomes the first x402-native capability registry for non-crypto agent needs — the "Stack Overflow for agents" that makes every new category agent-accessible from day one.
07
How Agents Discover Agent-First Platforms
For agents, "discovery" = machine-interpretable services, not human landing pages.
1. Protocol-level Discovery (x402 v2)
Services expose structured metadata (endpoints, pricing, chains). Facilitators crawl and index.
2. Facilitator/Registry Indexing
Layer of facilitators that index x402 services, maintain up-to-date pricing/metadata.
3. Agent-Centric Wallets
Coinbase agentic wallets pre-integrated with x402. Discovery APIs built-in.
4. Semantic Capability Registries
"Internet of Agents" research: agents announce capabilities in machine-interpretable form.
The agent doesn't "Google" a platform; it queries its facilitator ("find a market-data API with latency <100ms and price <0.5¢/request"), receives candidates with structured metadata, picks one, then talks HTTP+402 with it.
— Perplexity Research, Feb 2026
WORLD MODELS
🏭 Data Factory
Synthetic Data for
Controllable Video Models
Video models can generate stunning visuals but can't follow precise instructions. The bottleneck isn't compute or architecture — it's training data with exact state trajectories.
Data scaling plateaus at 200K-400K samples. The persistent ~15% gap between in-domain and out-of-domain performance isn't solvable with more data — it requires architectural changes AND structured training data.
— VBVR Paper (Wang et al.), Feb 2026
$5B+
Raised in world model space (2025-26)
20M hrs
NVIDIA Cosmos training data
50%
Model accuracy on physics (chance level)
97%
Human accuracy on same tasks
The Core Insight: Controllability Before Reasoning
Why Video Models Fail at Reasoning
1
Trained on natural video
Learned "everything moves together" — can't isolate changes
2
No state tracking
Can't represent "object A moved, B stayed" explicitly
3
Errors compound
Step 1 error → Step 2 error → reasoning chain breaks
What Data Factory Provides
✓
Exact state trajectories
Frame-by-frame ground truth of what changed
✓
Parameterized variations
Same action, different contexts — curriculum learning
✓
Physics-accurate simulation
Genesis/Isaac Sim backends for real dynamics
The Robot Arm Analogy
You can't teach a robot to cook if it knocks over the salt every time it reaches for the pepper. Same with video models: if they can't execute precise state transitions, chaining multi-step reasoning becomes impossible. Controllability is the prerequisite.
The "Data Factory" Architecture
📋
Domain Spec
"warehouse picking"
⚙️
Scene Generator
Parameterized templates
🎮
Physics Sim
Genesis / Isaac Sim
🎬
Video + Labels
State trajectories
📦
Training Dataset
LoRA-ready format
🏭 Vertical Templates
Pre-built scene generators for:
- Warehouse robotics
- Surgical verification
- Manufacturing QA
- Autonomous driving
⚡ Scale Economics
Genesis claims:
430,000x
faster than real-time simulation
🎯 LoRA-Ready Output
Direct fine-tuning:
- Wan2.1 / Wan2.2 compatible
- Rank 32 = startup compute
- ~$5K for domain model
Why This Wins
📊 The Moat
The "data factory" — parameterized generators + distributed workers — is the real competitive advantage. No productized version exists for vertical industries.
Network effect: Each vertical adds templates → attracts more customers → funds more verticals
💰 Business Model
- Per-video pricing: $0.01-0.10 per synthetic clip
- Dataset packages: $5K-50K per domain
- Enterprise: Dedicated capacity + custom templates
Gross margins >80% (compute is cheap vs. real data collection)
Timing
Genesis open-sourced Dec 2024. NVIDIA Cosmos launched Jan 2025. π0 open-sourced Feb 2025. The infrastructure just became available — but nobody has built the vertical data factory layer yet.
Competitive Landscape
| Company | Focus | Gap |
|---|---|---|
| NVIDIA Cosmos | Foundation models | Not vertical-specific data |
| Genesis AI | Physics engine | No data pipeline layer |
| Physical Intelligence | Robot foundation model | Consumes data, doesn't sell it |
| Scale AI | Data labeling | Labels real data, doesn't generate |
| Data Factory (Us) | Synthetic video data | Full vertical pipeline ✓ |
The dirty secret of robotics AI is that real-world data collection costs $100-1000/hour when you include robot time, human supervision, and failure recovery. Synthetic data at $0.01/clip changes the economics completely.
— Industry estimate
WORLD MODELS
📏 Eval Platform
Benchmarking Infrastructure
for World Models
VLM-as-a-judge is expensive and non-reproducible. IntPhys shows models at chance level. Everyone's flying blind on what their world models actually understand.
Most models perform at chance levels (50%), in stark contrast to human performance, which achieves near-perfect accuracy (97%+). Current video understanding benchmarks do not capture intuitive physics.
— IntPhys 2 (Meta FAIR), Jun 2025
50%
Best model accuracy (chance level)
97%
Human accuracy (same test)
47 pts
Gap to close
$5B+
Raised without rigorous eval
The Evaluation Crisis
Current State: Flying Blind
1
VLM-as-a-judge
Expensive ($0.10-1.00/sample), non-reproducible, biased
2
Demo-driven claims
Cherry-picked videos, no systematic testing
3
Benchmarks don't transfer
Academic benchmarks ≠ production reliability
What's Needed
✓
Deterministic scoring
Rule-based, reproducible, cheap to run
✓
Human-correlated metrics
VBVR-Bench achieves ρ > 0.9 with human judgment
✓
Domain-specific suites
Robotics, driving, medical — each needs own benchmarks
The VBVR Breakthrough
VBVR-Bench demonstrates that rule-based evaluation can match human judgment (ρ > 0.9 correlation). But it's research code, not a product. Domain-specific versions don't exist.
Product: Eval-as-a-Service
🎬
Upload Video
Model output
🔬
Benchmark Suite
Physics / Control / Reasoning
📊
Detailed Report
Scores + failure modes
📈
Leaderboard
Public or private
🧪 Benchmark Suites
- Physics: Object permanence, gravity, collisions
- Control: Instruction following, state isolation
- Reasoning: Multi-step causal chains
- Domain: Robotics, driving, medical
💰 Pricing
- API: $0.01/video (basic)
- Full suite: $0.10/video
- Enterprise: Unlimited + custom
- Leaderboard: Free tier for visibility
🎯 Target Customers
- Runway, World Labs, DeepMind
- Physical Intelligence, Wayve
- Robot startups building on π0
- Enterprise adopting video AI
Why This Works
📊 Market Dynamics
$5B+ has been raised in world models with no standardized evaluation. Every company is building their own benchmarks internally. That's waste.
Comparable: ML evaluation market ~$500M (2024), growing 25%+ YoY
🔄 Network Effects
- Leaderboard: Models compete → drives adoption
- Benchmark contributions: Companies add domain tests
- Data flywheel: More evals → better calibration
The gap between demo videos and production reliability is massive. Objects disappear, physics drifts, game logic is brittle over longer sessions. We need systematic evaluation, not cherry-picked demos.
— GradientFlow Analysis, 2026
Roadmap
Q2
Launch Core Benchmarks
IntPhys-style physics, VBVR-style controllability, basic API
Q3
Domain Expansion
Robotics suite (π0 compatible), driving suite (Wayve/Comma style)
Q4
Public Leaderboard
Like HuggingFace but for world models. Attract submissions, build community
2027
Enterprise + Certification
"World Model certified for X domain" — becomes industry standard
WORLD MODELS
🤖 Embodied AI Infra
Training Platform for
Robot Foundation Models
Training video/world models costs 10-100x more than LLMs. The infrastructure layer is missing. We build the "AWS for embodied AI."
Embodied AI training requires tight integration of simulation, rendering, and ML. Current cloud offerings are designed for LLMs. The infrastructure gap is massive.
— Industry observation
$1B
World Labs raise (Feb 2026)
$600M
Physical Intelligence raise
$315M
Runway raise (Feb 2026)
10-100x
Video vs LLM compute cost
The Infrastructure Gap
What LLM Infra Provides
✓
GPU clusters
H100s, A100s, optimized networking
✓
Training frameworks
PyTorch, JAX, distributed training
✓
Data pipelines
Text ingestion, tokenization, streaming
What Embodied AI Needs (Missing)
✗
Integrated simulation
Physics engine + renderer + ML in one loop
✗
Video data pipelines
Frame extraction, state annotation, streaming
✗
Sim-to-real transfer
Domain randomization, reality gap tools
Why This Matters Now
π0 just open-sourced. Genesis just launched. Cosmos is available. The building blocks exist but nobody has assembled them into a platform. Every robotics startup is duct-taping their own stack.
Platform Architecture
🎮 Simulation Layer
Managed Genesis/Isaac Sim instances
- One-click deployment
- Auto-scaling workers
- Pre-built environments
- 430,000x faster than real-time
📊 Data Layer
Video + state trajectory storage
- Frame-level annotations
- Streaming to training
- Version control for datasets
- Curriculum management
🧠 Training Layer
Optimized for video models
- Pre-configured for π0, Cosmos
- LoRA fine-tuning pipelines
- Distributed video training
- Eval integration built-in
🎯
Define Task
🎮
Simulate
📦
Generate Data
🧠
Train Model
📏
Evaluate
🤖
Deploy
Market Opportunity
📈 TAM Analysis
| World model companies (funded) | $5B+ raised |
| Robot startups (π0 ecosystem) | 100+ companies |
| AV companies (simulation needs) | 50+ companies |
| Enterprise robotics adoption | Growing 30%+ YoY |
Conservative estimate: $2B addressable market for embodied AI infrastructure by 2028
💰 Business Model
- Compute: GPU-hours (sim + training)
- Storage: Video dataset hosting
- Platform: Monthly SaaS for orchestration
- Enterprise: Dedicated clusters + support
Target: 40-60% gross margins (better than pure GPU cloud)
Competitive Position
| Player | Sim | Data | Train | Eval | Deploy |
|---|---|---|---|---|---|
| CoreWeave/Lambda | ✗ | ✗ | ✓ | ✗ | ✗ |
| NVIDIA Omniverse | ✓ | ~ | ✗ | ✗ | ✗ |
| Genesis (OSS) | ✓ | ✗ | ✗ | ✗ | ✗ |
| Weights & Biases | ✗ | ~ | ~ | ✓ | ✗ |
| Us (Full Stack) | ✓ | ✓ | ✓ | ✓ | ✓ |
The Integration Thesis
Embodied AI requires tight coupling between simulation, data, and training. Point solutions create friction. An integrated platform captures the full workflow — and the full margin.
01
Mission Control
Kubernetes for
AI Coding Agents
Deploy, coordinate, and govern fleets of Claude Code, Cursor, and Codex—so your team ships 10x faster with verification.
95%
of enterprise AI pilots fail to deliver ROI
We fix that.
02
The $40B Problem
Enterprise spent $30-40B on AI pilots. Most failed—not because the models are bad, but because nobody built the infrastructure to run them safely.
95%
AI Pilots Fail
MIT study: lack of context, poor verification, no adaptation. The agent isn't the problem—the harness is.
70%
Devin Task Failure
Answer.AI study: only 3/20 tasks completed. Best autonomous agent still needs orchestration.
$200-400
Real Cost of "$20" Plans
Cursor's hidden API fees surprise users. One team spent $8,000 on a "$200" plan.
0
Cross-Session Learning
No coding agent learns from failures. Same mistakes repeat every session. Zero organizational memory.
"The bottleneck is now having multiple agents at once."
— r/GithubCopilot, 2026
03
Four Pillars of Agent Fleet Management
Every failed AI pilot breaks down on one of these. Mission Control solves all four.
🎯
Task Alignment
Does the agent understand what you actually want? Intent verification, scope control, semantic diff between ask and interpretation.
✓
Verifiability
How do you know it's right? Automated pipelines: tests, security, quality gates. Verified before it touches production.
⚡
Ability
Can the agent actually do this? Route tasks to best-fit agent. Claude Code for reasoning, Cursor for flow, Codex for CI/CD.
🧠
Adaptability
Does the system learn? Auto-generate rules from corrections. One person's fix helps the whole team.
The Insight
The agent is commodity. The harness is moat. Everyone's building better agents—nobody's building the infrastructure to run 50 of them safely on a production codebase. We are.
04
How Mission Control Works
From chaos to coordination in four steps.
1
Task Decomposition
Complex request → atomic, verifiable steps with dependency graph
2
Smart Routing
Route each subtask to best-fit agent based on capability profiling
3
Parallel Execution
Multiple agents work simultaneously, conflicts prevented automatically
4
Verify & Learn
Automated verification, corrections become team-wide rules
Without Mission Control
-
❌
-
❌
Review everything
45+ minutes per agent PR, AI slop review fatigue
-
❌
Same mistakes repeat
No learning between sessions or team members
-
❌
Surprise bills
$20/mo → $400/mo actual spend
With Mission Control
-
✅
Unified dashboard
All agents in one view, automatic coordination
-
✅
Pre-verified PRs
12-minute reviews, confidence scores, semantic diffs
-
✅
Continuous learning
Auto-generate .cursorrules, team-wide improvement
-
✅
Predictable costs
Route to cheapest capable agent, budget alerts
05
Why Now, Why Us
Market Timing
-
🚀
Multi-agent is inevitable
VS Code 1.109 enables multi-agent dev. GitHub Copilot + Claude + Codex side-by-side. No one owns orchestration.
-
📈
YC is 50% AI agents
25% of YC companies have almost entirely AI-generated codebases. They need this.
-
🏆
Models peaked, harness didn't
Claude Code at 72.5% SWE-bench—best in class. The gap is now coordination, not capability.
Competitive Landscape
| Tool | Gap |
|---|---|
| Langfuse | Observes, doesn't orchestrate |
| LangSmith | LangChain lock-in, no self-host |
| Cursor/Devin | Single-agent, no coordination |
| Linear/Jira | Track humans, not agents |
| CrewAI | Framework-specific, not code-native |
We're agent-agnostic, code-native, verification-first.
The Moat
Three things compound: (1) Routing models improve with every task—network effects. (2) CI/CD integration is sticky—once you're in, you stay. (3) Verification layer builds trust—that's years of R&D, not a feature.
RECRUITMENT
👥 Recruitment Autopilot
10,000 Roles Go Unfilled Every Day.
We Fill Them While You Sleep.
The AI recruiting agency that takes roles from intake to offer — no humans in the loop until the interview.
$200B+
Global Staffing TAM
80%
Applications Never Get a Response
12 days
Avg Time-to-Fill → We Do 3
150%
Annual Hourly Worker Turnover
The Problem
"Every time I log into LinkedIn Recruiter I feel like I'm being mugged by Microsoft with a smile. The damn thing is $10k+ a year and for what?"
— r/recruiting
💸
$50-100K/year across 3-4 tools
LinkedIn Recruiter, Greenhouse, Gem, SeekOut — and still doing manual work
LinkedIn Recruiter, Greenhouse, Gem, SeekOut — and still doing manual work
🕳️
80% of candidates hit a black hole
"Greenhouse is a resume black hole I never hear back from"
"Greenhouse is a resume black hole I never hear back from"
📉
InMail response rates at all-time lows
"1 in 5 responds if lucky. Most devs auto-ignore us."
"1 in 5 responds if lucky. Most devs auto-ignore us."
🔍
Search is fundamentally broken
"You want a backend engineer with Python? Here are 300 customer success managers and a dentist."
"You want a backend engineer with Python? Here are 300 customer success managers and a dentist."
👻
44% of hires already in your ATS
Silver medalists rot while you pay to source new candidates
Silver medalists rot while you pay to source new candidates
⏱️
70-80% of recruiter time is automatable
10-15 hours per hire spent on tasks AI can do today
10-15 hours per hire spent on tasks AI can do today
The Opportunity
Why Now? LLMs crossed the threshold. GPT-4+ can do natural language search, personalized outreach, and conversational screening that doesn't feel robotic. Meanwhile, LinkedIn fatigue is peaking — 43% of recruiters actively seeking alternatives (up from 26% in 2024).
Market Size
📊
$200B+ TAM — Global staffing market
🏭
80% is hourly roles — Warehouse, retail, logistics
📈
100-150% annual turnover — Constant hiring demand
Competitive Landscape
🦄
Mercor ($10B) — Proved model, pivoted to AI training
🎯
Juicebox ($200M) — PLG sourcing, not full-funnel
🏢
Paradox ($1.5B) — Enterprise only ($25K+)
The Gap: No one owns full-funnel automation for the SMB/mid-market. Enterprise tools are too expensive. Point solutions don't talk to each other. The hourly hiring market is wide open.
The Solution
One AI agent that owns the entire candidate journey — source to offer.
1️⃣
INTAKE (5 mins)
Natural language role description → AI generates ideal candidate profile + search plan
Natural language role description → AI generates ideal candidate profile + search plan
2️⃣
SOURCE (Autonomous)
AI searches 30+ sources, scores/ranks candidates, resurfaces silver medalists from ATS
AI searches 30+ sources, scores/ranks candidates, resurfaces silver medalists from ATS
3️⃣
OUTREACH (Autonomous)
Multi-channel sequences (SMS, email, WhatsApp), handles replies and FAQs automatically
Multi-channel sequences (SMS, email, WhatsApp), handles replies and FAQs automatically
4️⃣
SCREEN (Semi-Autonomous)
AI conducts phone/video screens overnight, scores against rubric, surfaces interview-ready candidates
AI conducts phone/video screens overnight, scores against rubric, surfaces interview-ready candidates
5️⃣
SCHEDULE (Autonomous)
Coordinates across calendars, handles reschedules and no-shows, sends prep materials
Coordinates across calendars, handles reschedules and no-shows, sends prep materials
6️⃣
CLOSE (AI-Assisted)
Comp benchmarks, offer letter generation, acceptance tracking
Comp benchmarks, offer letter generation, acceptance tracking
"We screen 50 candidates overnight. Recruiters wake up to 8 interview-ready."
GTM & Traction
Phase 1: Vertical Wedge
🎯
Target: Regional staffing agencies (10-50 recruiters) in warehouse/logistics
💰
Budget: $25K-50K/year in tooling spend
🤝
Offer: "Fill 20% of roles in 60 days or free"
Pricing Model
📋
$199/mo base — Platform access
✅
$500/successful hire — Aligned incentives
Target Metrics (Demo Day)
3 days
Time-to-fill (vs 12 baseline)
60%
Lower cost-per-hire
3x
Higher response rate than InMail
100 reqs
Per recruiter (vs 15)
Traction Narrative: "5 staffing agencies. 47 roles filled. Average time-to-fill: 4 days (baseline was 14). They're paying $500/hire and saving $2K vs previous process. $100K ARR run rate."
The Ask
$2M Seed
Expand to 50 agencies by end of year
Use of Funds
👥
Hiring: 3 engineers, 2 sales
🔧
Product: AI phone screen, multi-channel outreach
📈
GTM: ASA events, content, case studies
12-Month Milestones
🎯
50 agency customers
💵
$1M ARR
📊
10,000+ roles filled
🏆
Series A ready at $150-200M
"We're the AI agency for hourly hiring. $200B market, no full-stack autopilot exists. We built it."
CONSULTING
📊 Consulting Autopilot
The 70% Problem
Consulting is 70% intelligence work (research, analysis, modeling) and 30% judgment work. We automate the 70%—the nights and weekends work—so consultants can focus on what actually matters.
$300B+
Global Consulting TAM
60-90%
Time Spent on Manual Research
27%
Work Immediately Automatable
$116B
AI Consulting Market by 2035
PROBLEM
McKinsey Analysts Shouldn't Be PowerPoint Jockeys
"At McKinsey, we spent over 90% of our time on manual work—reading reports, building Excel models, creating presentations." — Grasp Founders
10+
Tools Per Project (copy-paste hell)
50%
Slide Time Spent on Formatting
$50K+
AlphaSense Cost Per Seat
"AI generates generic fluff, not MBB-quality output. Copilot can't do action titles. No tool understands Pyramid Principle or MECE structures."
INSIGHT
Sell TO Consultants, Not Replace Them
Operand says "AI to Kill McKinsey." We say: give McKinsey analysts superpowers. The winning model is augmentation—Grasp has 200 customers and 3.5x ARR growth proving it.
Grasp
$9M raised, 200 customers, augmentation
Perceptis
"Superpowers, not replacement"
2-3x
More Proposals, 40-70% Win Lift
The Killer Gap: Verifiability. Consultants don't trust AI outputs. Every claim needs traceable sources, explicit assumptions, confidence intervals. No one does this yet.
SOLUTION
AI Analysts for Consulting Firms
Multi-agent system that turns days of research into client-ready deliverables in hours. Research → Analysis → Deliverable—one unified workflow with human-in-the-loop verification.
Research Agent
20+ sources, synthesized, every claim cited
Analysis Agent
Models, scenarios, visualizations
Deliverable Agent
MBB-quality decks, Pyramid Principle
Verification Layer
Source links, confidence scores, audit trail
Why Now: Multi-modal AI finally good enough. Reasoning models enable complex analysis. Consulting under cost pressure. Talent arbitrage—ex-MBB available to train AI and verify outputs.
GTM
Start With Boutiques, Not Big Four
10-50 person consulting shops. Ex-MBB founders who know what "good" looks like. Fast decision cycles, desperate for competitive advantage, can't build in-house AI.
10-50
Person Firms (Sweet Spot)
$2-50M
Revenue Range
$200-500
Per Seat/Month Target
SOC-2
Early (Enterprise Must-Have)
Expansion Path: Boutiques → In-house corporate strategy teams → PE portfolio companies → Big Four individual teams → Enterprise. Grasp already serves "most of the Big Four" after starting narrow.
ASK
Building the AI Operating System for Consulting
We're Cursor for consultants—AI that actually understands strategy, not just generates slides. Targeting $2M seed to build the verifiable, consulting-grade AI workflow that doesn't exist yet.
80%
Research Time Reduction Target
3.5x
ARR Growth (Grasp Benchmark)
10x
Cheaper Than AlphaSense
$100B+
US Gov Consulting Spend (YC RFS)
One-Liner: "We're building AI analysts for consulting firms. Our multi-agent system turns days of research into client-ready deliverables in hours—with every claim verifiable and humans always in control."
Vertical Deep Dive • Supply Chain & Procurement
📦 OpenHolly
Autonomous Procurement Agents
That Replace $180B in Human Labor
We're building AI agents that do the work of procurement BPOs at 1/20th the cost. Pay-per-outcome, not seats.
18:1 Labor vs Software Spend
90% CPOs Exploring Agents
Incumbents at 1.2/5 ⭐
Enterprises spend over $180 billion annually on procurement talent, compared to roughly $10 billion on procurement software, reflecting how much work still happens manually around existing systems.
— Lio PR / Industry Analysis
The 18:1 Arbitrage
When companies spend 18x more on people than software, the work is clearly being done by humans. That's our opening.
$180B
Annual spend on procurement talent
Human labor doing manual work
$10B
Annual spend on procurement software
Tools that don't actually do the work
The Insight
Software hasn't automated procurement — it's just digitized paperwork. The work is still done by humans. AI agents can now do that work.
90%
CPOs considering AI agents
ProcureCon 2025
$5.8B
Procurement BPO market
Already outsourced = proven spend
2%
Spend leaks annually
McKinsey — unfulfilled obligations
58%
Struggle to find talent
CIPS 2025
The Incumbent Disaster
SAP Ariba is the market leader. Users hate it. This is our opening.
⭐ 1.2 / 5 on Trustpilot
SAP Ariba — 98 reviews, near-universal hate
"This software makes me wanna quit my job. This should not exist."
— SAP Ariba User, Trustpilot
"Logs me out 5720937 times a day... like software from 1980"
— SAP Ariba User, Trustpilot
"If you are a supplier, THIS WILL HURT YOUR BUSINESS."
— SAP Ariba User, Trustpilot
Why Incumbents Can't Fix This
🐌
Legacy architecture
Built pre-cloud, can't rebuild without breaking everything
💸
Misaligned incentives
Revenue from supplier fees, not buyer value
🔧
IT-heavy configuration
6-12 month implementations, constant IT involvement
🤷
Support is non-existent
"Tell me it's not their department"
What Buyers Actually Want
- Fast implementation (days, not months)
- Consumer-grade UX
- Pay for outcomes, not seats
- Measurable ROI from day one
GTM Playbooks That Work
The winners in this space have proven wedges and go-to-market strategies we can learn from.
🎯 Zip — The Cold DM Strategy
Before building anything, founders DMed hundreds of procurement managers on LinkedIn — not to sell, but to learn.
- Avoided selling to anyone they knew (tested true PMF with strangers)
- Customers "ready to buy before the demo was even complete"
- 4 customers in 6 months of product development
Why It Worked
Built for a "boring, massive" market with "sleepy incumbents" and "low NPS"
💰 Magentic — Pay-Per-Cure
Zero upfront cost. "You never pay for seats or shelfware." Implementation in days, not months.
- If no savings, no payment — complete risk reversal
- Found 25% of supplier docs had errors impacting P&L
- $10-20M P&L impact per customer
Sequoia Thesis
"In the old world, SaaS sold the promise of ROI. In the new world, AI actually delivers it."
🤖 Pactum — Tail Spend Wedge
Started with "long tail" suppliers enterprises never negotiate with — low risk, high volume.
- Walmart pilot: 89 suppliers, 5 buyers, 3 months
- $25K proof-of-concept pricing
- 68% supplier acceptance, 3% avg savings
- 75% preferred negotiating with bot over human
👥 Lio — BPO Displacement
Positioned as replacing outsourcing, not software. Competes against BPOs (20x cost of software).
- 75% of outsourced work automated in 6 months
- 95%+ adoption rate, 100% retention
- 10 FTE freed per customer
- German enterprise entry (Munich Re, Schaeffler)
Where We Win
The four-pillars framework shows exactly where agentic AI outperforms existing tools.
| Area | Legacy Gap | Agentic Opportunity |
|---|---|---|
| Intake & Approval | Email chains, manual routing | Autonomous triage and routing |
| Negotiation | Human-only, can't scale | AI negotiation at scale (2000 suppliers simultaneously) |
| Contract Compliance | Periodic audits, 2% value leakage | Continuous monitoring, proactive alerts |
| Supplier Management | Reactive, manual tracking | Proactive risk sensing, autonomous action |
| Invoice Processing | OCR + human review | End-to-end matching and payment |
White Space We Target
🏭
Mid-market
Coupa too expensive, Ariba too painful
📄
Supplier compliance
Wide open — Magentic early stage
🇺🇸
US industrial
Tacto owns Germany, US gap
🏥
Vertical-specific
Healthcare, construction procurement
Our Entry Wedge
Managed Procurement Ops
Like our SDR offering, but for procurement. We operate the agents, customers get outcomes.
- Start with supplier compliance (Magentic model)
- Pay-per-savings pricing
- Expand to negotiation, sourcing
Why Now
🤖 Capability Threshold
LLMs can now read contracts, negotiate, and execute end-to-end. Pactum proved it at Walmart scale.
🌍 Supply Chain Polycrisis
COVID, Ukraine, tariffs created urgency. "The old answers—another dashboard or SaaS tool—are spent."
👥 Talent Shortage
58% struggle to find/retain procurement talent. BPO arbitrage is ending as AI gets cheaper than offshore labor.
"We're entering a phase in the enterprise where AI moves beyond workflow co-pilots to autonomous, multi-agent execution."
— Seema Amble, a16z
Investment Thesis
$180B of human labor waiting to be automated.
Incumbents at 1.2/5 stars. 90% of CPOs exploring agents.
✅ High intelligence-to-judgment ratio • ✅ $5.8B BPO proves willingness to pay • ✅ Measurable P&L impact • ✅ Sequoia + a16z conviction
HIGHLY ATTRACTIVE vertical for AI autopilot investment
Vertical • Insurance Brokerage
🏢 Insurance Brokerage
Insurance Is 95% Coordination,
5% Judgment.
39,000 agencies run on email and spreadsheets. We're building the AI that replaces their back office — delivering outcomes, not tools.
$140-200B TAM
YC RFS Vertical
3 Unicorns Emerging
Getting one of these businesses insured takes ~50 steps over two weeks. The broker's actual judgment matters for maybe 5% of the process. The other 95% is pure coordination.
— Panta (YC W26)
YC & VCs Are All-In on This Vertical
Sequoia, Emergence, Khosla, and YC are funding AI-native brokerages. YC's 2026 RFS explicitly calls out "AI-Native Agencies."
"Agencies of the future will look more like software companies, with software margins. And they'll scale far bigger than any agencies that exist in these fragmented markets today."
— YC Request for Startups, Spring 2026
Recent Funding
🚀
WithCoverage
700+ clients in 18 months • Growth startups (GoPuff, Bombas, EightSleep)
$42M Series B
Sequoia, Khosla, 8VC
🎯
Harper
5,000+ clients • Middle America SMBs (daycares, dealerships, restaurants)
$47M Series A
YC W25 • Emergence, Peak XV
⚡
Panta
Hard-to-place E&S risks • Trucking, nightclubs, construction
YC W26
In production Dec 2024
Pattern
All three prove the same thesis: AI can handle the 95% coordination layer, freeing brokers for the 5% that actually requires judgment.
Brokers Are Drowning in Coordination
The industry runs on copy-paste, portal juggling, and endless email chasing.
67%
Access 5+ carrier portals weekly
Glia 2024 Agent Report
76%
Can't find/access markets to quote
IVANS 2025
2+ hrs
Average underwriter response time
Glia 2024
79%
In carrier portal when needing help
Glia 2024
The 50-Step Process
📝
Intake & Forms
ACORD forms, questionnaires, documentation
📤
Submission to Carriers
Submit to 5-20 carriers per risk
🔄
Chase Underwriters
Follow-up emails, answer questions
📊
Compare & Present
Quote comparison, gap analysis
✅
Bind & Service
COIs, endorsements, renewals
Why It's Ripe for AI
🤖
Structured & Repetitive
Same 50 steps, every single time
📋
Document-Heavy
PDFs, forms, emails — all parseable
🔗
API Access Exists
1,000+ carrier APIs via IVANS
💬
Communication-Centric
Email/chat = agent-native
⏰
Time-Sensitive
Speed = competitive advantage
Managed AI Ops for Insurance Brokers
We don't sell software. We deliver outcomes: placements, renewals, COIs — done.
🎯
Broker Goal
"Place 40 accounts/month"
⚡
Our Agent Fleet
Submissions, follow-up, quotes
🤖
Autopilot Execution
50 steps automated
✅
Bound Policies
10x capacity, same staff
AI SDR Tools (Clay, Apollo, etc.)
🛠️
You configure and manage
Become pseudo-IT for AI
📉
70% churn in 3 months
Tools don't deliver outcomes
❓
No domain expertise
Generic tools, generic results
OpenHolly Managed Ops
✅
We run the agents
You focus on advising clients
🎯
Outcome-based pricing
Pay per placement, not per seat
🏢
Insurance-native playbooks
ACORD forms, carrier APIs, COIs
The Panta Insight
One broker: 400 clients. One AI-augmented broker: 4,000 clients.
99% placement rate vs industry ~60%. Quote turnaround: days → hours.
GTM: Start with Mid-Market Agencies
35,000 agencies with <$2M revenue have no AI tools. They can't afford Zywave. We're their answer.
🎯 Target Segment
Mid-market agencies ($1-10M revenue)
- Sophisticated enough to understand value
- Small enough to decide quickly
- Large enough to pay real money
- Hungry for competitive edge
🚪 Entry Points
- Free hook Insurance portfolio analysis
- Partner Aggregators (Smart Choice, Keystone)
- Direct State association chapters (Big "I")
- Referral Accountants serving SMBs
💰 Unit Economics
- $5-50K avg commercial premium
- 10-15% commission per policy
- $500-5K revenue per placement
- 70%+ gross margin (vs 30% trad)
WithCoverage Playbook
"Thousands of calls, travel across dozens of states. Offer free insurance analysis showing overpayment. In 18 months, 700+ of the fastest growing companies switched to us." — Max Brenner, CEO
Why Now: Perfect Storm
🤖 AI Capability Inflection
- Multimodal models read PDFs, emails, forms
- Agent frameworks enable 50-step automation
- 1,000+ carrier APIs finally available (IVANS)
📉 Fragmented Market Ready
- 39K agencies → 38K (consolidating)
- 1/3 expect ownership changes in 5 years
- Most run on "email and spreadsheets"
🐢 Incumbents Are Slow
- Zywave just launched AI (Dec 2025)
- Applied Systems: "augmentation not replacement"
- PE-owned = margin focus, not innovation
✅ Proven Demand
- WithCoverage: $42M, 700+ clients
- Harper: $47M, 5,000+ clients
- Panta: YC W26, in production
The Opportunity
$140-200B
Broker TAM (Sequoia)
39K
Agencies Underserved
95%
Work That's Automatable
The playbook is proven. The market is massive. The incumbents are asleep.
Vertical: IT Managed Services (MSP)
🖥️ OpenHolly for MSPs
AI IT Support That Resolves Tickets,
Not Just Routes Them
We automate 50% of IT helpdesk tickets in week one. Incumbents are hated. 67% of orgs can't hire enough techs. The market is ready.
$100B+ TAM
90% L1 Automatable
ConnectWise/Kaseya Hated
"Faster to automate a task forever than to do it manually once."
— Serval's Core Pitch ($127M raised, $1B valuation)
The Problem: Chronic Technician Shortage
MSPs can't hire enough techs. Their tools are outdated. AI can finally solve L1.
67%
Organizations understaffed on IT/security
52%
MSPs say hiring is primary struggle
3.5M
Global cybersecurity workforce gap
90%
L1 tickets ServiceNow resolves autonomously (internal)
The Insight
Can't hire fast enough → must automate. L1 is now automatable. The gap is who does it for SMBs.
Incumbents Are Hated
ConnectWise and Kaseya have created massive dissatisfaction. MSPs are actively seeking alternatives.
🔴 ConnectWise
- "Archaic UX" — looks like 2010 software
- "Little to zero updates for 10+ years"
- "Reporting is essentially garbage"
- SSO security debacle damaged trust
- Market share: 26.8% → 24.3% (losing)
🔴 Kaseya / Datto
- "Chronically overcharge clients for thousands of dollars per year"
- Billing errors are systemic, not one-off
- CEO minimizes problems, lies about fixes
- Acquired good companies → quality dropped
- "Dollar store ConnectWise"
"ConnectWise is less bad overall"
— r/msp (damning with faint praise)
Structural issues: PE ownership → cost-cutting, poor support, billing games, technical debt. They can't rebuild AI-native. We can.
$100B+ Market, Nobody Owns It
$100B+
Global MSP TAM
50%+
Tickets automatable (proven by Serval)
$1B
Serval valuation (enterprise only)
Where We Play
| Segment | Status |
|---|---|
| Enterprise ($100K+ ACV) | Serval owns this |
| SMB Direct (50-500 employees) | Blue ocean |
| Small MSPs (5-20 techs) | Good entry wedge |
Why Now
🤖
L1 is now automatable
GPT-4+ enables reliable action execution
📉
Incumbent lock-in weakening
ConnectWise/Kaseya losing share, MSPs looking
🏗️
AI-native architectures possible
Code-gen enables custom workflows
The Playbook: Start AI-Native, Own the SMB
Serval proved it with enterprise. We do it for the rest of the market.
🚀
Phase 1
Small MSPs (5-20 techs)
💰
Phase 2
SMBs direct ($30-80/employee)
🎯
Endgame
IT-as-a-service for SMBs
What We Automate (Week 1)
🔑
Password resets (15-25% of tickets)
90%+ automatable
🔐
Access provisioning (15-20%)
Okta, Google Groups, SCIM
📦
Software install/config (10-15%)
Standard apps, self-service
👋
Onboarding/offboarding (10-15%)
Day 1 automation
Competitive Advantage
| Us | Them |
|---|---|
| AI-native architecture | Bolt-on AI to legacy |
| Code-based workflows (auditable) | Black-box AI |
| Outcome pricing (per ticket) | Seat-based (pay even if unused) |
| Deploy in days | 6-month implementations |
The Ask
🎯 Target Design Partners
- Small MSPs (5-20 technicians)
- Pain with ConnectWise/Kaseya billing
- Drowning in L1 tickets
- Open to AI (not defensive)
Distribution: r/msp, IT Nation, MSP peer groups
💰 Pricing Model
- Entry Per ticket resolved — prove ROI fast
- Scale Per technician — $149-209/tech/mo
- SMB Direct Per employee — $30-80/mo
Start free, pay when we hit 30% automation.
The Thesis
Every SMB outsources IT to MSPs. MSPs are dissatisfied with ConnectWise/Kaseya. 50%+ of tickets are automatable.
Nobody is selling "your IT just runs" directly to SMBs as an outcome.
Serval raised $127M doing this for enterprise. The SMB market is unowned.
"The IT team that scales with you, without headcount."
Vertical • Healthcare RCM
🏥 OpenHolly
Autopilot for Medical Billing
We recover millions in denied claims. AI agents that fight back against payer denials — so hospitals can focus on patients, not paperwork.
Healthcare RCM
$50-80B TAM
11.8% Denial Rate
Payers Using AI to Deny
One insurer allegedly denied 300,000 claims in under two months using AI. Providers need their own AI to fight back.
— Healthcare Industry Report, 2024
The Problem: Payers Are Winning
Hospitals are drowning in denials. Payers deploy AI to reject claims faster. Providers still fight with spreadsheets.
11.8%
Initial claim denial rate (up from 10.2%)
2024 Industry Data
$25-181
Cost to rework each denied claim
HFMA
60%
Providers can't hire RCM staff
Becker's Healthcare
300K
Claims denied by one payer AI in 2 months
Industry Report
⚠️ The Arms Race
Payers are deploying AI to deny claims faster. Medicare Advantage denials up 4.8% YoY. Providers need AI to fight back — or they lose.
Why Now: $50-80B Opportunity
Market Size
💰
$50-80B outsourced RCM
Massive existing spend, ripe for automation
📈
Growing denial complexity
89% saw PA requirements increase in 2024
👴
40%+ coders retiring
Massive labor shortage, no backfill coming
Why AI Wins Now
🧠
LLMs can read 50K-word records
Finally capable of clinical document understanding
📋
150K+ ICD-10 codes now tractable
AI accuracy approaching human coders
⚖️
CMS mandating electronic PA
Regulatory tailwind forcing digitization
The Insight
Outcome-based pricing is native to healthcare — providers already pay % of collections. We align incentives: we only win when they recover money.
Competitive Landscape
Legacy vendors are slow. AI-native players are enterprise-only. The mid-market is wide open.
| Company | Focus | Strengths | Gap |
|---|---|---|---|
| Anterior ($64M+) | Prior auth (payer-side) | 99.24% accuracy, KLAS validated | Payer-only, not provider-side |
| AKASA | Provider RCM (enterprise) | Cleveland Clinic, Stanford | 12+ month sales cycles, expensive |
| Fathom | Medical coding only | 95.5/100 KLAS score | Narrow focus, no denial mgmt |
| Waystar / R1 | Legacy platforms | Scale, integrations | "AI" is mostly marketing, slow |
| OpenHolly | Denial recovery | Outcome-based, fast deploy | — |
Our Wedge: Denial Management
Start with the most measurable outcome: dollars recovered from denials.
15-20% of recovered revenue. We only win when you recover money.
How It Works: Agentic Denial Recovery
AI agents that read charts, identify denial root causes, generate appeals, and submit — automatically.
📥
Denial Arrives
Payer rejects claim
🤖
AI Reads Chart
50K words in 8 seconds
📝
Appeal Generated
Clinical evidence cited
✅
$ Recovered
You keep 80-85%
🔍 Root Cause Analysis
AI identifies why denial happened — missing documentation, coding error, or arbitrary payer rule.
📋 Evidence Extraction
Highlights relevant clinical passages from 50K-word records in seconds.
⚡ Auto-Appeal
Generates payer-specific appeal letters with guideline citations. Human reviews in 5 minutes.
Go-to-Market & The Ask
Entry Strategy
🎯
Start: Mid-size specialty practices
Orthopedics, cardiology, oncology (high-value procedures)
🔑
Wedge: Denial recovery audit
Low risk entry — AI is checking, not deciding
📈
Expand: Full denial management
Then coding QA, prior auth automation
Pricing Model
💰 Outcome-Based
15-20%
of recovered revenue from denials
You only pay when we recover money. Zero risk.
Looking For: Design Partners
3-5 specialty practices with 500+ denials/month
We'll recover $100K+ in year one — or you pay nothing.
Outcome-Based
Zero Risk Trial
HIPAA Compliant
Vertical • Accounting & Audit
📊 OpenHolly
AI That Closes the Books
While Accountants Sleep
340K accountants left the profession. 75% of CPAs are retiring. The close still takes 6+ days. We're building the autopilot.
$50-80B TAM
94% Still Use Excel
Basis: $1.15B Validation
"Accounting is structured, high-stakes, and essential to every business on earth. It's also one of the most underbuilt areas in technology."
— Basis founders (valued at $1.15B)
The Perfect Storm
A profession in crisis meets primitive tooling. Something has to give.
340K
Accountants left since 2020
Bureau of Labor Statistics
75%
CPAs nearing retirement
AICPA/NASBA 2025
-30%
CPA exam candidates since 2016
Industry data
120K+
Open jobs per year
Ramp 2026
The Tooling Problem
📊
94% still use Excel for close
50% cite it as key reason close is slow
⏱️
50% take 6+ business days to close
Only 18% close in 3 days or less
💸
20-50 hrs/month on cash reconciliation
#1 bottleneck — 3-5 systems just to match
Bench Collapse Proves the Thesis
"Bench raised ~$160M. Shut down Dec 2024. Human-heavy bookkeeping models can't scale profitably."
— Industry lesson
🔥 "Bench Refugees" = Urgent Demand
Thousands of abandoned customers actively looking for alternatives. Distrust human-heavy models. Ready for AI-first.
Market Validation: $1.15B Proves the Opportunity
Basis raised $100M Series B at $1.15B valuation in Feb 2026. The market is real.
🦄 Basis ($1.15B)
30% of Top 25 US firms already using
20% of Top 150 firms
First AI agent to complete end-to-end 1065 tax return autonomously
$100M Series B, Feb 2026
📈 Rillet (a16z + ICONIQ)
200+ customers, Series B
AI-native ERP — not AI bolted onto legacy
"Go live in weeks" vs months
Doubled ARR quarter-over-quarter
🔍 Truewind ($17M)
Top 50 accounting firm partners
100+ customers
"Absorbs 47% of month-end close tasks"
Series A, Dec 2024
The TAM
$50-80B market for accounting automation
Every business needs accounting. It's recurring. It's essential. And it's still done by hand.
The Autonomous Close Agent
AI that runs overnight, completes 90% of month-end tasks, and generates an exception report for morning review.
🌙
6 PM
Run Autonomous Close
🤖
Agent Fleet
Works through 30+ tasks
☀️
8 AM
Exception report ready
✅
Review & Sign
5-10 items vs 100+
What the Agent Does
🏦
Bank Reconciliation
Auto-matching across all sources (95% accuracy)
📑
Transaction Classification
90%+ accuracy with LLMs, learns from corrections
📊
Accrual Workpapers
Auto-generated with supporting documentation
📈
Variance Analysis
AI explains anomalies, humans review
The Outcome
| Metric | Before | After |
|---|---|---|
| Close Time | 6+ days | <3 days |
| Cash Rec Hours | 20-50 hrs | <5 hrs |
| Manual Tasks | 80% | 10% |
| Error Rate | 1-5% | <0.5% |
Your Accountant Reviews the Work Instead of Doing the Work
Everything is auditable. Everything is documented. AI generates, humans verify.
Why Now
🧠 AI Capability Inflection
- LLMs now achieve >90% accuracy on transaction classification
- Basis demonstrated first autonomous 1065 tax return (Feb 2026)
- Multi-hour autonomous agent workflows proven possible
📉 Structural Talent Crisis
- 340K+ accountants left since 2020
- 75% of CPAs nearing retirement
- Projected enrollment decline of 15% (2025-2029)
Why Incumbents Can't Catch Up
🏚️
NetSuite is 25 years old
QuickBooks architecture is rigid
🤖
Adding chatbots, not agents
Rillet raised $50M+ because "rebuilding from scratch" is the only way
💼
Big Four won't build this
Services business, not software. Internal innovation killed by billable hour model.
Our Entry Point
🔥
Bench Refugees
Urgent need, distrust of human-heavy, ready for AI-first
📈
Mid-Market Outgrowers
$10-100M revenue, 2-5 person finance teams drowning in close
🏢
Small CPA Firms (100-500 clients)
Fast adoption, price sensitive, willing to try
The Opportunity
$50-80B
Total Addressable Market
$1.15B
Basis valuation (market validation)
340K
Accountants gone = demand for AI
Pricing Model
Per-Entity Most common for accounting software
Volume Tiers Common for AP/AR automation
Outcome-Based Emerging: % of cost savings
$2K – $15K/mo
Based on entity count + transaction volume
ICP: Design Partners
🏢
$10-100M revenue
Outgrowing QuickBooks, avoiding NetSuite
👥
2-5 person finance team
Drowning in close process
🏗️
Multi-entity structures
SaaS, e-commerce, hospitality
AI That Closes the Books While Accountants Sleep
The talent crisis is permanent. The tooling is primitive. The AI is ready.
We're building the autopilot for accounting.
Vertical Pitch • Claims Adjusting
🔥 OpenHolly
Autopilot for Insurance Claims
We sell the adjustment, not the software. 400K workers retiring. $50-80B market. AI-native TPAs are the future.
$50-80B TAM
$730M Exit
400K Retiring
Sequoia Top Pick
Services: The New Software. The biggest opportunity isn't selling tools to adjusters — it's replacing what adjusters do.
— Sequoia Capital, March 2026
The Perfect Storm
Structural workforce crisis meets broken incumbent tech.
400K
Insurance workers retiring by 2026
25% of workforce is 55+
70%
AI SDR users churn in 3 months
Industry data
18-24
Months to implement Guidewire
Industry standard
$730M
EvolutionIQ acquired (Dec 2024)
CCC acquisition
"Half a billion between software, personnel, and opportunity cost" for Guidewire implementations that still fail.
— Industry Analysis
The Reality
Within 15 years, a large portion of today's adjusters will have retired — and there won't be enough people to replace them.
Why Now: Market Validation
Smart money is flooding in. Exits are happening. The window is open.
💰
EvolutionIQ → CCC: $730M
December 2024. AI claims automation is now a proven exit category.
| Company | Model | Funding | Traction |
|---|---|---|---|
| Strala | AI-native TPA | Founders Fund (13x oversubscribed) | 26 US clients, UK expansion |
| Pace | Operations automation | Sequoia $10M Series A | Prudential multi-year deal |
| Elysian | Complex commercial claims | AmFam Ventures $6M | State Farm pitch winner, Lloyd's Lab |
| Tractable | Photo AI (point solution) | $1B+ unicorn | Auto insurers, property |
The Gap
Tractable sells photo AI. Shift sells fraud detection. Nobody sells the full claim outcome — FNOL to settlement, end-to-end.
Entry Strategy: TPAs & MGAs
Start where decisions are fast and budgets already exist.
Why TPAs/MGAs First
⚡
Already outsourced
Budget line exists. Vendor swap, not new category.
🏃
Faster decisions
Not 12-24 month enterprise sales cycles
💰
Undercut by 30-50%
Per-claim pricing below legacy TPA rates
📈
Expand upward
Use TPA wins to land large carriers
What We Deliver
📞 FNOL Intake Agent
↓
📋 Coverage Verification
↓
🔍 Investigation + Fraud
↓
💵 Estimate + Settlement
↓
✅ Claim Closed
The Strala Playbook
Start with FNOL/triage → Hybrid deployments → Full TPA as trust builds
"The answer can't always be more people." — Strala
The Numbers That Matter
Outcome-based pricing. Clear ROI story.
⏱️ Cycle Time
Industry avg
30+ days
Top performers
10 days
AI Target
3-5 days
💰 Cost per Claim
Legacy TPA
$250-1,500
Our pricing
30-50% less
+ Outcome bonus
Per day saved
📊 Claims Volume
Human adjuster
50-100/month
AI agent fleet
500+/month
Loss ratio impact
-1-2pp
Pricing Model
- Per Claim 30-50% below legacy TPA rates
- Outcome Bonus +$X per day under cycle target
- Pilot Free/discounted on subset → expand
Loss Ratio Impact
Strala claims 1 point loss ratio improvement. That's the number carriers care about most.
— Industry benchmark
The Opportunity
🎯 Why This, Why Now
- $50-80B TAM in claims adjusting spend
- 400K workers retiring, pipeline empty
- $730M exit validates category
- Sequoia + Founders Fund backing competitors
- Guidewire "half billion" disasters create opening
🚀 Our Edge
- Fat startup model — outcomes, not tools
- Playbooks compound — each customer teaches us
- Per-claim pricing — undercut legacy by 30-50%
- TPA wedge — fast sales, expand upward
"Autopilot for insurance claims — we sell the adjustment, not the software."
The workforce is retiring. We're what comes next.
$50-80B Market
Validated by $730M Exit
Perfect Timing
TAX ADVISORY
📋 Tax Advisory Autopilot
The CPA Extinction Event
75% of CPAs are reaching retirement. 340,000 accountants left the profession since 2020. But tax work is 80-90% pure intelligence work—the exact work AI agents do best. We're building autopilot for tax preparation.
$30-35B
US Tax Preparation TAM
80-90%
Intelligence Work (Automatable)
340K
CPAs Left Profession (2020-22)
75%
CPAs Nearing Retirement
PROBLEM
Tax Season Is Breaking People
"Endless hours, stressed teams, client overload, constant risk of missing deadlines." 42% of firms report retention issues from burnout. The people who do stay work 60-80 hour weeks for months.
42%
Firms Report Retention Issues
60-80
Hours/Week During Tax Season
61.5%
Say Price Is #1 Complaint
"Difficulties with state returns came up repeatedly in 'dislike' responses. Multi-state complexity multiplies fast, and manual tracking of different state rules becomes impossible at scale."
INSIGHT
Multi-State Is the Unsolved Problem
Incumbents charge $60K+/year for seat licenses. Blue J achieved 12x revenue growth via CPA.com distribution. But nobody has cracked multi-state complexity—nexus determination, varying apportionment rules, threshold tracking.
Blue J
12x Revenue via CPA.com Partnership
$60K+
UltraTax Annual Cost
50
States, Each With Different Rules
The Killer Gap: Research tools sell per-seat. Preparation is still manual. Nobody sells completed returns. The outcome-based pricing model is wide open.
SOLUTION
AI Agents That Prepare Returns
Multi-agent system: reads documents, applies firm's tax strategy, enters data into systems. What takes 4 hours becomes 15 minutes of review. Every citation verifiable. Human signs, AI does the work.
Document Agent
Extracts from K-1s, statements, invoices
Research Agent
IRC citations, state rules, confidence scores
Prep Agent
Drafts returns, flags items for review
SALT Agent
Multi-state nexus, apportionment, thresholds
Why Now: GPT-4 enabled Blue J's 12x growth. Filed claims 30-50% review cycle reduction. Avalara building "agentic tax" for transaction compliance. The capability inflection is here.
GTM
Start Mid-Market, Pay Per Return
6-50 preparer firms: fast decisions, acute talent pain, can't build in-house. Outcome-based pricing—firms pay for completed returns, not software seats. CPA society partnerships for distribution.
6-50
Preparer Sweet Spot
Per-Return
Pricing (vs $1,500/Seat)
400K+
AICPA Members (Distribution)
Weeks
Not Months to Close
Expansion Path: Mid-market firms → State CPA society endorsements → Enterprise (Top 100) → Big Four white-label. Basis already has 30% of Top 25 with enterprise-first approach.
ASK
Autopilot for Tax Preparation
Blue J sells research tools to accountants. We sell completed tax returns to firms. Outcome-based pricing aligned with Sequoia's "sell the work" thesis. The demographic crisis is now—we're the solution.
70%
Prep Time Reduction Target
7d → 1d
Turnaround (Filed Benchmark)
30-50%
Faster Review Cycles
12x
Blue J Revenue Growth (Comp)
One-Liner: "AI agents that prepare tax returns from scratch—firms pay per return, not per seat. We automate the 80% of tax work that's pure intelligence, so the retiring 75% of CPAs don't take the industry with them."
LEGAL TRANSACTIONAL
⚖️ Legal Autopilot
Your Legal Team Is Buried in NDAs.
We Handle Them Autonomously.
The autonomous legal team for scaling companies — starting with NDAs.
$20-25B
Legal AI TAM
$11B
Harvey Valuation
12+ mo
Avg CLM Implementation Time
28%
Legal Teams Want Contract Review AI First
The Problem
"For something as simple as correcting a typo, you have to download the template, correct it in Word, and re-upload. Initial setup was a nightmare — took nearly a year while we paid for the service."
— G2 Reviews on CLM Platforms
💸
Harvey: $1,200/seat/month
20-seat minimum, 12-month commits. Built for BigLaw, not scaling companies.
20-seat minimum, 12-month commits. Built for BigLaw, not scaling companies.
⏳
CLM implementations take 12+ months
"Implementation partner was required and it took a year longer than promised"
"Implementation partner was required and it took a year longer than promised"
🔍
Search is broken
"Can't search by PO# or SKU#" — business-relevant metadata ignored
"Can't search by PO# or SKU#" — business-relevant metadata ignored
💼
Crosby: ~$400/contract
Great for funded startups, but too expensive for volume
Great for funded startups, but too expensive for volume
🐌
3-5 day turnarounds on simple NDAs
Legal bottlenecks literally slow down expansion and kill deals
Legal bottlenecks literally slow down expansion and kill deals
📄
Word dependency everywhere
Clunky downloads, re-uploads, version control nightmares
Clunky downloads, re-uploads, version control nightmares
The Opportunity
Why NDAs? Highest volume contract type. Most standardized. Lowest risk of catastrophic error. Already outsourced by many companies ($13B+ LPO market). Sales teams hate waiting for legal. It's the perfect wedge.
Market Size
📊
$20-25B TAM — Legal AI market
🏭
$13B+ LPO market — Already outsourced, ripe for automation
📈
40% of enterprise apps — Will have AI agents by 2026 (Gartner)
Competitive Landscape
🦄
Harvey ($11B) — BigLaw only, $1,200/seat
🎯
Crosby — Slack-native, ~$400/contract
🏢
Ironclad/DocuSign CLM — Heavy implementation, enterprise only
The Gap: No one owns autonomous contract processing for the 90% of companies that can't afford $400/contract or 12-month CLM implementations. Harvey's GTM? "Provocative demos attacking lawyers' own work." Ours? Ship in 30 minutes, not 30 days.
The Solution
One AI agent that handles contracts from intake to signed — Slack-native, no lawyers needed for routine work.
1️⃣
INTAKE (30 seconds)
Sales drops NDA in Slack → AI acknowledges and starts processing
Sales drops NDA in Slack → AI acknowledges and starts processing
2️⃣
ANALYZE (Autonomous)
AI identifies deviations from company playbook, flags risk levels
AI identifies deviations from company playbook, flags risk levels
3️⃣
REDLINE (Autonomous)
Auto-generates redlines with clear reasoning, ready to send back
Auto-generates redlines with clear reasoning, ready to send back
4️⃣
ESCALATE (Smart)
Only flags items that truly need human review — <15% of contracts
Only flags items that truly need human review — <15% of contracts
5️⃣
NEGOTIATE (Semi-Autonomous)
Handles counter-party responses, multiple rounds without human input
Handles counter-party responses, multiple rounds without human input
6️⃣
TRACK (Autonomous)
Monitors obligations, alerts before renewals, auto-drafts compliance comms
Monitors obligations, alerts before renewals, auto-drafts compliance comms
"This used to take 3 days. Now it's 47 minutes. They've processed 200 NDAs this quarter, attorney touched 12."
GTM & Traction
Phase 1: NDA Wedge
🎯
Target: Series A-C startups with aggressive GTM (100-500 employees)
😤
Pain: "Legal bottlenecks literally slowing down expansion"
🤝
Distribution: VC portfolio network (Crosby playbook)
Pricing Model
📋
$50/contract — 8x cheaper than Crosby
✅
$2K/mo flat — Unlimited NDAs for high-volume teams
Target Metrics (Month 12)
25K
Contracts Processed
<30 min
Turnaround Time
<15%
Attorney Review Rate
200%
Expansion Rate
Traction Narrative: "5 companies. 1,000+ contracts processed. Turnaround time: 47 minutes (baseline was 3 days). Cost per contract: $50 vs $400 at Crosby. NPS: 78. $150K ARR run rate."
The Ask
$2M Seed
Own the autonomous legal stack for scaling companies
Use of Funds
👥
Hiring: 3 engineers, 1 legal ops
🔧
Product: MSA support, playbook builder, AI negotiation
📈
GTM: VC partnerships, legal ops communities
12-Month Milestones
🎯
100 company customers
💵
$1.5M ARR
📊
50,000+ contracts processed
🏆
Expand to MSAs, DPAs, vendor agreements
"Crosby for the 90% of companies that can't afford $400/contract. We're the autonomous legal team for scaling companies — starting with NDAs."