Every AI Company Claims to be Different
In a market where every target claims proprietary AI, investors face a critical challenge: distinguishing genuine technical capability from well-marketed wrappers around commodity models.
The stakes are significant:
- •Overvaluation based on AI claims that don't survive technical scrutiny
- •Hidden technical debt that becomes your problem post-acquisition
- •"Key person" dependencies masked by impressive demos
- •Moats that evaporate when foundation models commoditise
Traditional due diligence wasn't built for this. Legal and financial reviews can't tell you whether the AI is real.
Technical Due Diligence by Someone Who's Built It
Dr Colin Kelly
I've been on every side of AI investment decisions: building AI products, leading research teams, advising boards, and assessing technical claims for major deals.
This isn't theoretical assessment. I know what good AI looks like because I've shipped it. I know what breaks at scale because I've fixed it. I know what genuine defensibility looks like because I've built moats, and watched others crumble.
A Framework Built from Practice
Six critical dimensions of AI investment assessment
Technical Reality
Is this genuine AI or a thin wrapper? Automated code analysis plus human review of architecture, model choices, and implementation quality.
Defensibility & Moat
Will this advantage persist? Assessment of data assets, proprietary training, switching costs, and vulnerability to foundation model commoditisation.
Scalability
What breaks at 10x? Review of infrastructure, cost dynamics, and technical debt that compounds with growth.
Team Capability
Can they execute the roadmap? Evaluation of technical leadership depth, key person dependencies, and realistic delivery capacity.
Data & Governance
Where's the risk? Assessment of data provenance, privacy compliance, and responsible AI practices.
Commercial Alignment
Does the tech serve the business? Gap analysis between technical capability and commercial claims.
Why Investors Trust This Assessment
PhD in Natural Language Processing, University of Cambridge: Original research in extracting knowledge from unstructured text
Head of Applied AI Research, AI Defence Platform: Built and led the team delivering production AI capabilities
AI Value Assessment Experience, IBM: Identified €50m+ in AI-driven benefits across telco, financial services, and automotive sectors
Founder, Committee for Responsible AI: Developed ethical risk frameworks now used in production systems
Fractional CTO: Currently guiding technical architecture for AI product builds
Invited Speaker, Cambridge MSt in AI Ethics & Society: Trusted voice on AI capability and governance
"I don't just assess AI—I've built it, deployed it, governed it, and advised boards on it. That's the difference." -- Colin Kelly, PhD (Cambridge), Agathon Founder
Deliverables That Drive Decisions
Standalone Technical Assessment Report including:
Executive Summary
Clear investment recommendation framing
Technical Reality Assessment
Is the AI genuine and defensible?
Risk Register
Severity ratings and mitigation options
Scalability Analysis
What breaks, when, and what it costs to fix
Team & Capability Evaluation
Can they deliver what they promise?
Competitive Positioning
How does their tech compare to alternatives?
Questions for Management
Issues requiring founder/CTO clarification
Optional add-on: Integration with your legal and financial DD workstreams
Reports are custom to each engagement: no templated box-ticking.
Two Weeks to Clarity
Week 1: Discovery & Analysis
- •Document review (technical documentation, architecture diagrams, pitch materials)
- •Automated code analysis using proprietary assessment methods
- •Data room review where available
Week 2: Validation & Reporting
- •Technical interviews with founders/CTO
- •Challenge sessions on key claims
- •Report drafting and delivery
- •Debrief call with your investment team
Typical engagement: 2 weeks | Confidential | NDA-protected
Considering an AI Investment?
Every week you spend uncertain about technical claims is a week your competitors might be moving faster.
Or download our free checklist to get started
Frequently Asked Questions
How quickly can you start?
Typically within one week of engagement confirmation.
Do you need source code access?
Preferred but not required. We can deliver meaningful assessment from documentation, demos, and technical interviews alone. We can also assess based on available materials and flag gaps as risks.
What if the target company is uncooperative?
I can assess based on available materials and flag gaps as risks. Reluctance to share technical details is itself a finding.
Do you work under NDA?
Always. All engagements are fully confidential.
Free Download: The AI Due Diligence Checklist
12 critical questions every investor should ask before committing to an AI investment
Download the AI Due Diligence Checklist
Get instant access to our comprehensive checklist: 12 critical questions investors should ask before any AI investment.