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Technical Due Diligence for Investors & Acquirers
Technical due diligence is an independent engineering review of a target company's codebase, architecture, team capability, and technical narrative - typically for VCs pre-investment, PE roll-ups, or acquirers - priced at $1,250 as a fixed one-off with findings investors can act on within one to two weeks.
What is technical due diligence?
Technical due diligence (tech DD) answers one question for investors and acquirers: can this engineering organization deliver what the pitch deck claims, and what will it cost to get there?
Unlike financial DD, tech DD evaluates architecture decisions, code health signals, team depth, security basics, AI claims versus reality, and whether the technical story in the data room matches what is in the repo.
What I review
Reviews are structured for decision-makers who need clear signal fast, not a 200-page PDF no one reads. Depth scales with access: read-only repo access, architecture docs, and 1-2 calls with the CTO or lead engineer are typical.
- Architecture and scalability: monolith vs. services, data model, bottlenecks, build-vs-buy decisions.
- Code health: test coverage signals, dependency risk, deployment frequency, incident patterns.
- Team and process: bus factor, seniority mix, hiring plan realism, velocity indicators.
- AI claims: separate production AI from demo-ware; model costs, eval coverage, data pipelines.
- Security baseline: secrets handling, auth model, dependency vulnerabilities, compliance gaps.
- Technical debt and roadmap: what breaks at 2× users, 10× users; realistic remediation cost.
Deliverables
You receive a concise written report with a traffic-light summary per area, specific findings (not vague 'needs improvement'), and recommended questions for management. Calls to walk through findings with the deal team are included.
- Executive summary (1 page): invest / pass / investigate further with top 3 risks.
- Detailed findings by area with evidence and severity.
- AI-specific appendix when the target claims ML/LLM capabilities.
- Follow-up call with investor or acquirer team.
Why an operator, not a Big Four checklist
I run a team of 12 engineers shipping production AI systems today: TypeScript, Next.js, Python, FastAPI, Postgres, LLMs, MCP. DD reports reflect how startups actually build in 2026, not a generic enterprise audit template.
Proof points
- Production systems: media localization ~18k hours/day, long-form dubbing pipelines
- Stack fluency: TypeScript, Python, FastAPI, Postgres, LLMs, MCP in production
Related writing
FAQ
How long does tech DD take?
Typically one to two weeks from kickoff to written report, depending on repo size and data-room responsiveness. Urgent timelines can be discussed on the discovery call.
Do you need full repo access?
Read-only access to the main application repo, architecture docs, and 1-2 engineering interviews is the minimum. More access yields higher-confidence findings.
Can you evaluate AI claims specifically?
Yes. I assess whether AI features are production-grade (evals, cost controls, fallbacks) or demo-ware, based on code and infrastructure signals.
Who is this for?
VCs doing pre-investment or follow-on DD, PE firms rolling up software assets, and acquirers evaluating engineering risk in a target.
Next step
30-minute discovery call: review fit and disqualify honestly. A one-page scope doc follows within a few days if we proceed.