For Investors
Independent technical diligence you can trust before you wire.
Seed funds, angels, venture partners, and family offices hire me for a fast, credible read on the technology, the architecture, and the team, so you understand execution risk before you invest, and ask sharper questions along the way.
The Deliverable
A clear memo, not a black box.
You get an independent technical diligence memo written in plain language: feasible vs. hand-wavy, real moat vs. marketing, and where execution risk actually lives.
Typical deliverables
- Technical diligence memo
- Architecture feasibility review
- Team capability assessment
- Moat and defensibility notes
- Low / Medium / High risk flags
- Key questions to ask before investing
How It Works
Fast when you need it, deep when it counts.
Diligence is scoped to your investment timeline — a quick red-flag read in days, or a deeper memo with interviews. We size it on a short call.
When to bring me in
- Before issuing a term sheet
- During confirmatory technical diligence
- When the thesis rests on AI / CV / simulation / deployment feasibility
- When you need sharper questions for the founder or CTO
- When the team lacks deep robotics / cloud / AI expertise in-house
What I need
- Pitch deck
- Architecture diagrams
- Product demo
- Technical docs & team bios
- Customer / pilot context
- A 60–90 minute founder / CTO call
Turnaround
- Fast read: a red-flag memo in days
- Deep dive: a full assessment on your timeline
- Interviews & architecture walkthrough as needed
- Scope and timing sized on a short call
How I Flag Risk
Every finding gets a clear, honest rating.
Each finding is rated Low, Medium, or High Risk, with the reasoning behind it, so the memo is unambiguous and easy to skim for your whole partnership.
Solid and defensible
Architecture is realistic, the team can execute, and the approach holds up under scrutiny. Proceed with normal follow-up.
Watch and verify
Workable, but with assumptions that need testing: scaling, cost, hiring, or timelines. Worth specific questions before and after the round.
Material concern
A gap that could break the thesis: infeasible architecture, missing capability, or a moat that isn't there. Flagged clearly, with the reasoning.
What I Evaluate
The layers where AI and robotics deals actually break.
Product & Architecture
Is it real?
- Architecture feasibility and realism
- AI / LLM / CV approach vs. state of the art
- Sim-to-real and edge-case exposure
- What's demoware vs. production
Infrastructure & Cost
Will it scale?
- Cloud, Kubernetes, and deployment risk
- Inference cost, latency, and reliability
- Observability and operational maturity
- Security posture and data handling
Team & Execution
Can they build it?
- Team capability vs. the roadmap
- Key-person and hiring-sequencing risk
- Engineering velocity and technical debt
- Moat and defensibility
I've built and led the exact systems you're diligencing: robotics simulation at Amazon Lab126, cloud robotics infrastructure and teams at Formant, the global content pipeline behind every title on Amazon Prime Video, and cloud infrastructure for deployed service robots at Savioke.
That means I can tell the difference between a hard problem the team has genuinely solved and a slide that sounds impressive. I evaluate feasibility, execution, and team the way a hands-on operator would, because I've had to make those calls with my own capital of time and reputation.
Engagements are independent and confidential.
For investors
Sharper questions before you invest.
I take on a limited number of diligence engagements each quarter for AI, robotics, computer vision, cloud infrastructure, and embodied-AI companies.