AI Companies All Claim Technical Differentiation
Every pitch deck claims "proprietary AI." Every demo looks impressive. Every founder sounds confident about their technical moat.
The reality: most AI implementations wrap commodity APIs with minimal innovation. The impressive demo hides architecture that won't scale, won't differentiate, and won't survive competition from anyone with actual technical depth.
Legal and financial due diligence can't assess AI technical capability. Without deep technical expertise, distinguishing real innovation from marketing is nearly impossible.
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
Is This Right For You?
This service is designed for:
Investors (VC, PE, family offices) evaluating AI-focused companies before making significant investments
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
We provide honest assessment based on technical evidence, regardless of what you hope to hear.
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
Do the technical capabilities actually support the revenue model and growth claims? Gap analysis between what the tech can do and what the pitch deck promises.
What We Actually Assess
| Surface Claim | What We 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 Trust This Assessment
Dr Colin Kelly brings direct experience from building, scaling, and governing AI systems, and not just assessing 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: 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
"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 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
Phase 1: Scope Definition
Day 1
We align on your investment thesis, specific concerns, and what would change your decision. This ensures we focus on what actually matters for your decision, not generic assessment criteria.
Phase 2: Technical Deep-Dive
Days 2-6
Document review, automated code analysis, data room review, technical interviews with founders/CTO, and capability demonstrations with independent testing.
Phase 3: Analysis & Synthesis
Days 7-8
We synthesise findings against your investment thesis. What's real? What's hype? What are the risks? What opportunities might they be missing?
Phase 4: Report & Debrief
Days 9-10
Comprehensive written report and interactive debrief session with your investment team.
Typical engagement: 2 weeks | $15,000-30,000 depending on scope | Expedited timelines available | Confidential | NDA-protected
Related Insights
AI governance: what business leaders need to know
Most organisations are building AI compliance theatre whilst competitors build capability fortresses, treating governance as bureaucratic overhead rather than the competitive advantage that enables sustainable AI deployment.
Richard Sutton's bitter lesson explains why your AI solution feels shallow
Sutton's bitter lesson reveals that most AI implementations feel shallow because they prioritize domain expertise over computational scale, leaving roughly 80% of potential untapped.
Should I build my own large language model (LLM)?
Organizations considering building their own large language models (LLMs) should weigh the benefits of control and specialisation against challenges like high computational needs and expertise requirements.
Considering an AI Investment?
Delays in technical assessment can cost competitive advantage.
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. We can deliver meaningful assessment from documentation, demos, and technical interviews alone.
What if the target company is uncooperative?
Reluctance itself is informative. We 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 key differences: First, we've built production AI systems, not just assessed them — our evaluations come from hands-on experience shipping AI products, not 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 identified, along with estimated remediation costs and timelines. We 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 provide investment recommendations?
We provide technical assessment and risk analysis. We don't make investment recommendations — that decision depends on factors beyond technical capability. We give you the technical truth; you make the business decision.
Free Download: AI Investment Red Flags
12 warning signs that indicate you need expert technical assessment
Download the AI Investment Red Flags Guide
The red flags that internal teams and generalist advisors consistently miss — and why they matter for your investment decision.