Founders & engineering teams
Prototype-to-Production Risk Review
A broad review for teams whose demo works but whose production plan needs pressure-testing — architecture, scaling, cost, security, and execution gaps.
Learn moreTechnical Consulting · Physical AI · Robotics · Cloud
Technical consulting for AI, robotics, and cloud-native startups.
I'm Abraham. I help founders, engineering teams, and investors pressure-test AI, robotics, and cloud infrastructure — before technical risk turns into a missed timeline, a failed pilot, or a bad investment. Every engagement is a fixed-scope review that ends in a plain-language memo and a prioritized 30/60/90-day plan.
No pitch, no commitment. Just a conversation to see if I'm the right person to help.
Former Head of Engineering at Formant. Former Amazon Lab126 robotics simulation leader. Former Director of Cloud Infrastructure at Savioke. 15+ years across robotics, simulation, cloud infrastructure, AI/ML systems, AR, and consumer devices.
Illustrative example Every engagement ends in a memo like this: findings risk-rated, then turned into a prioritized 30 / 60 / 90-day plan. The areas reviewed vary by engagement.
15+
Years across robotics, simulation & cloud
6
U.S. patents in haptics & sensing
1,000s
Robots operated at fleet scale
8
Companies shipped at, incl. Amazon & Formant
Experience across
Amazon Lab126
Amazon Prime Video
Formant
Savioke
CastAR
Immersion
BlackBerry
The Problem
Your demo works. Will your production system?
AI and robotics products often fail after the demo, when real customers, real data, real infrastructure, and real-world edge cases expose hidden risks.
Common failure points include fragile deployments, expensive inference, weak observability, unrealistic simulation, unclear scaling assumptions, missing security posture, poor hiring sequencing, and technical debt that blocks customers or investors.
- Fragile deployments
- Expensive inference
- Weak observability
- Unrealistic simulation
- Unclear scaling
- Missing security posture
- Poor hiring sequencing
- Blocking technical debt
I help teams identify those risks early and turn them into practical next steps.
What You Get
A concrete deliverable, not a slide deck.
- A plain-language technical risk memo
- Low / Medium / High risk ratings on every finding
- A prioritized 30 / 60 / 90-day action plan
- An optional follow-up working session with your team or investors
Best fit when
- Your demo works, but production risk is unclear
- You're preparing for a fundraise, pilot, or enterprise rollout
- You need an independent technical read before investing
- Your AI/robotics architecture is getting fragile, expensive, or hard to operate
What I Help With
Three ways I reduce technical risk.
Fixed-scope, senior reviews — not staff augmentation. Each ends in a practical, prioritized memo you can act on.
Technical teams
AI, Robotics & Cloud Infrastructure Review
A hands-on review of the systems that carry AI and robotics to production — LLM/CV infra, cloud & Kubernetes, edge/on-prem, simulation, and fleet deployment.
Learn moreInvestors
Investor Technical Diligence
An independent, plain-language read on feasibility, team, and execution risk for investors in AI, robotics, computer vision, cloud, or embodied AI.
Learn moreSpecialty areas within these reviews
LLM / CV systems
Robotics simulation & sim-to-real
Fleet operations & teleoperation
Cloud-native architecture
Kubernetes / CI-CD
Edge / on-prem deployment
Technical hiring & org design
How Engagements Work
A short, focused process. Three steps.
Step 01
Fit Call
A short conversation to understand the product, architecture, team, and current risks, and to confirm I'm the right person to help.
Step 02
Focused Review
I review architecture diagrams, technical docs, product plans, deployment workflows, pitch materials, or diligence materials.
Step 03
Actionable Memo
You get a practical assessment with risks, recommendations, and prioritized next steps: what to fix now vs. later.
Example Engagements
What a typical review looks like.
- 01 Review an LLM/CV deployment architecture before enterprise rollout.
- 02 Pressure-test a robotics simulation strategy before scaling field pilots.
- 03 Design a deployment strategy for a multi-robot fleet across cloud, edge, and on-prem.
- 04 Evaluate technical risk before a seed investment.
- 05 Help a founder decide what technical hires to make next.
- 06 Review cloud/Kubernetes architecture for cost, reliability, and operational risk.
- 07 Prepare technical materials for investor or customer diligence.
- 08 Identify what will break between demo, pilot, and production.
Who I Work With
Founders, engineering teams, and investors.
You might be here because the prototype works but the path to production is murky, because an architecture is getting fragile or expensive, or because you need an independent read before you invest. Any of those is a good reason to talk.
Founders
Seed & growth-stage
- The prototype works, but production risk is unclear
- Architecture is becoming fragile or expensive
- Preparing for fundraising or customer diligence
- Deciding what to build, buy, hire, or defer
Engineering Teams
Outside expert review
- Scaling issues and cloud-cost concerns
- Weak observability, deployment complexity
- Simulation gaps and technical debt
- Edge / on-prem uncertainty
Investors
Fast, credible diligence
- Understand architecture and execution risk
- Sharper questions before investing
- Independent view of team and feasibility
- Low / Medium / High risk flags
I'm Abraham Dauhajre. I've spent 15+ years at the boundary of software and the physical world, the domain now called physical AI, building robotics, simulation, cloud, and AI/ML systems.
Previously, I led robotics simulation at Amazon Lab126, led the global content-metadata pipeline behind every title on Amazon Prime Video, and served as Head of Engineering at Formant, a cloud robotics startup, where I helped build cloud robotics infrastructure and engineering teams from the ground up.
Across 15+ years I've worked at the boundary of software and the physical world, physical AI: robotics, simulation, computer vision, cloud infrastructure, AR, AI/ML systems, and consumer devices. That range is what lets me spot the risks that only show up when a demo meets reality.
Get in touch
Need senior technical help?
I take on a limited number of consulting, diligence, and expert advisory engagements for AI, robotics, cloud infrastructure, and physical-world technology companies.
No pitch, no commitment. Just a conversation to see if I'm the right person to help.