AI Companies All Claim Technical Differentiation
Every pitch deck claims "proprietary AI." Every demo looks impressive. Every founder sounds confident about their technical moat.
Most AI implementations wrap commodity APIs with little innovation. The impressive demo hides architecture that won't scale, won't differentiate, and won't survive competition from anyone with real technical depth.
Legal and financial due diligence can't assess AI technical capability. Without deep technical expertise, telling real innovation from marketing is nearly impossible.
The same blindness applies inside companies you already own. Portfolio businesses carry AI opportunity nobody has mapped and AI debt nobody has priced: brittle pilots that never reached production, vendor commitments that don't fit the operating model, and a plan built on what suppliers pitched rather than what the business can absorb.
The stakes:
- •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
- •Value-creation plans that assume AI capability the company doesn't have, at a cost nobody has estimated
Two Questions, One Assessment
The engagement takes two forms, depending on where you sit relative to the company. The method is the same six-dimension assessment; the question it answers is different.
Pricing a deal
Is the target's AI real, defensible, and worth what the valuation assumes? I assess the technology before you commit capital, so the AI claim in the deck is priced on evidence.
Typical trigger: a term sheet, an LOI, or an investment committee asking questions the data room can't answer.
Building value in a company you own
Where can AI genuinely create value in this business, what AI debt is already on the books, and what does capturing the opportunity actually cost? I assess the company's systems, data, and team against a realistic plan, not a vendor's pitch.
Typical trigger: a hold-period value-creation plan, an operating partner review, or an AI initiative that has stalled and nobody can say why.
Is This Right For You?
This service is for:
Investors (VC, PE, family offices) evaluating AI-focused companies before making significant investments
Private equity firms and operating partners assessing AI readiness and value-creation potential in companies they already own, or are acquiring for operational reasons
Corporate M&A teams assessing AI capabilities as part of acquisition due diligence
Executives considering major AI vendor commitments or platform bets
Boards who need independent technical assessment before approving significant budgets
I provide honest assessment based on technical evidence, regardless of what you hope to hear.
A Framework Built from Practice
Six dimensions, whether you're pricing a deal or building value in a company you own
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? I assess data assets, proprietary training, switching costs, and exposure to foundation model commoditisation.
Scalability
What breaks at 10x? I review infrastructure, cost dynamics, and technical debt that compounds with growth.
Team Capability
Can they execute the roadmap? I evaluate technical leadership depth, key person dependencies, and realistic delivery capacity.
Data & Governance
Where's the risk? I assess data provenance, privacy compliance, and responsible AI practices.
Commercial Alignment
Do the technical capabilities support the revenue model and growth claims? I find the gap between what the tech can do and what the pitch deck promises.
What I Actually Assess
| Surface Claim | What I Actually Assess |
|---|---|
| "Proprietary AI" | Custom models, fine-tuned, or API wrapper? |
| "Unique data moat" | Is the data genuinely differentiated and defensible? |
| "Advanced ML capabilities" | Sophisticated architecture or basic implementation? |
| "Scalable platform" | Will it actually scale, or will it break at 10x load? |
| "Strong technical team" | Can they build what's needed for the next 3 years? |
| "First-mover advantage" | Technical moat or just temporary lead? |
Why Investors and Operators Trust This Assessment
Dr Colin Kelly has built, scaled, and governed AI systems, not just assessed them.
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: Built ethical risk frameworks now used in production systems
Hands-on builder: I'm architecting production AI systems now, so my assessments reflect how AI is built today
Invited Speaker, Cambridge MSt in AI Ethics & Society: Trusted voice on AI capability and governance
"My assessment comes from building, deploying, and governing AI systems across research and production environments." -- Colin Kelly, PhD (Cambridge), Agathon Founder
Deliverables That Drive Decisions
Standalone Technical Assessment Report including:
Executive Summary
Clear framing for your deal decision or value-creation plan
AI Opportunity Map
Where AI creates value in the business, and what capturing it costs
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 needing founder or CTO clarification
Optional add-on: Integration with your legal and financial DD workstreams
Every report is custom to the engagement. No templated box-ticking.
Two Weeks to Clarity
Phase 1: Scope Definition
Day 1
I align with you on the question the assessment must answer — the investment thesis for a deal, or the value-creation plan for a company you own — plus your specific concerns and what would change your decision.
Phase 2: Technical Deep-Dive
Days 2-6
Document review, automated code analysis, data room review, technical interviews with founders and CTO, and capability demonstrations with independent testing.
Phase 3: Analysis & Synthesis
Days 7-8
I synthesise findings against your thesis or value-creation plan. What's real? What's hype? What are the risks? What opportunities is the business missing?
Phase 4: Report & Debrief
Days 9-10
Written report and a live debrief with your investment or operating team.
Typical engagement: 2-6 weeks, scoped to the deal or asset | From $15,000 | Expedited timelines available | Confidential | NDA-protected
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Or download the red flags guide 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. Ideal: codebase access, architecture documentation, technical team interviews, live system access. Minimum: technical team interviews and live demonstrations. I can deliver meaningful assessment from documentation, demos, and technical interviews alone.
What if the target company is uncooperative?
Reluctance itself is informative. I can assess based on available materials and flag gaps as risks. Sometimes the most important finding is what they won't show you.
Do you work under NDA?
Always. All engagements are fully confidential.
How is this different from Big 4 or standard technical due diligence?
Three differences. First, I've built production AI systems, not just assessed them, so my evaluations come from shipping AI products rather than theoretical frameworks. Second, this is AI-specific depth, not generalist technical DD that treats AI as one checkbox among many. Third, you get direct access to the person doing the assessment, not a partner who sells and a junior team who delivers.
What if the assessment reveals significant problems?
The report includes severity ratings for every issue, along with estimated remediation costs and timelines. I help you understand whether issues are deal-breakers, negotiation points, or post-acquisition fixes. A negative finding isn't necessarily a reason to walk away. It's information that affects valuation and deal structure.
Do you assess companies we already own?
Yes. The same six-dimension assessment applies, pointed at a different question: instead of "is this target's AI worth the price", it answers "where can AI create value in this business, what AI debt is already on the books, and what would capturing the opportunity cost". This suits hold-period value-creation planning, operating partner reviews, and initiatives that have stalled without a clear diagnosis.
What does a value-creation assessment deliver?
An AI opportunity map grounded in the company's actual systems, data, and team rather than vendor claims; a register of existing AI debt with severity and remediation costs; and a sequenced view of what to fix, build, or buy first. The output is a plan your operating team can execute, with the costs stated before you commit budget.
Do you provide investment recommendations?
I provide technical assessment and risk analysis. Investment recommendations fall outside scope, because that decision depends on factors beyond technical capability. I give you the technical truth; you make the business decision.
Free Download: AI Investment Red Flags
12 warning signs that you need expert technical assessment
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The red flags that internal teams and generalist advisors miss, and why they matter for your investment decision.