Technology Due Diligence
Independent technology due diligence for investors evaluating technology companies. We translate technical complexity into investment decisions — because we've built the kind of technology you're evaluating.
Why Technology Due Diligence Matters
In technology-driven deals, the technical assessment can make or break the investment thesis. Is the AI actually in production or just a demo? Is the architecture scalable or held together with duct tape? Does the team have the depth to execute the plan? These questions have enormous implications for valuation, risk, and post-acquisition strategy.
Most generalist DD firms lack the technical depth to properly assess AI and ML capabilities. And most technical assessors lack the commercial context to translate their findings into investment decisions. We bridge this gap — because we've built enterprise AI and we understand how investors think.
Our Due Diligence Framework
Architecture & Infrastructure
System architecture review, cloud vs on-premise assessment, scalability analysis, technical debt quantification, and critical dependency mapping. We evaluate whether the technology foundations can support the business plan.
AI/ML Assessment
Model inventory and maturity assessment, data provenance and quality review, benchmark analysis, production monitoring evaluation, retraining processes, and build-vs-buy analysis. We separate genuine AI capability from marketing claims.
Team & Capability
Engineering team structure and depth, key person risk identification, talent pipeline assessment, retention analysis, and skill gap mapping. Technology is only as strong as the team behind it.
IP & Data Assets
IP ownership verification, data rights and licensing review, proprietary dataset evaluation, competitive moat assessment, and open-source dependency audit. We identify what's truly defensible.
Red Flags
We check for the 10 most common red flags in technology companies, including demo-only AI, single-model dependency, lack of production monitoring, undocumented technical debt, and misrepresented capabilities. Each red flag includes severity assessment and remediation cost estimate.
What You Get
An investment-ready memo with traffic light risk assessment across all five areas, detailed findings with supporting evidence, specific questions for management follow-up, remediation cost estimates for identified issues, and a verbal briefing to discuss implications.
Our memos are designed for investment professionals. Technical depth where it matters, commercial translation throughout. Clear enough to inform IC decisions without requiring a technical background.
Download the Tech DD Checklist
The same due diligence checklist our team uses when evaluating technology companies. Covers architecture, AI/ML, team, IP, and red flags.
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Frequently Asked Questions
What does technology due diligence cover?
Our DD covers five areas: Architecture & Infrastructure (system design, scalability, tech debt), AI/ML Specific (model inventory, data provenance, production readiness), Team & Capability (engineering structure, key person risk), IP & Data Assets (ownership, moats, dependencies), and Red Flags (common issues we see in AI companies).
How quickly can you deliver?
Standard turnaround is 5 working days. Our 48-hour express service covers the critical assessment areas for time-sensitive deals. Both include a written memo and verbal briefing.
Do you need access to the codebase?
Code access significantly improves the depth of assessment, but we can deliver valuable insights from architecture documentation, demos, and management interviews. We adapt our methodology based on what's available.
What types of deals do you typically support?
We support PE buyouts, VC growth rounds, M&A transactions, and corporate development evaluations. Deal sizes typically range from £5M-£200M. The common thread is technology complexity that requires specialist assessment.
Can you present to our investment committee?
Yes. We regularly present DD findings to ICs, translating technical findings into commercial implications that inform investment decisions.
What are the most common red flags you find?
The most common issues include: demo-only AI that isn't in production, single-model dependency with no fallback, lack of production monitoring, key person risk concentrated in one engineer, undisclosed open-source dependencies with licensing implications, and inflated AI claims in marketing materials.
