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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/ML 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 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 & scalability — monolith vs. services, data model, bottlenecks, build-vs-buy decisions.
- Code health — test coverage signals, dependency risk, deployment frequency, incident patterns.
- Team & process — bus factor, seniority mix, hiring plan realism, eng velocity indicators.
- AI/ML 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 & 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
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(s), architecture docs, and 1–2 engineering interviews is the minimum. More access yields higher-confidence findings.
Can you evaluate AI/LLM claims specifically?
Yes — this is a common gap in generic DD. 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 — fit and disqualify honestly. Scope doc within a few days if we proceed.