# Technical Due Diligence Checklist for Startup Investors (2026)

> What to inspect in startup tech DD beyond generic templates: architecture, team depth, AI claims, and security — with signals that actually kill deals in 2026.

**Author:** Muneeb Hussain · Fractional CTO & AI Advisor
**Published:** 2026-05-20
**Last updated:** 2026-05-26

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## Why generic templates fail

Generic tech DD templates miss what actually kills deals in 2026: AI claims that aren't production-grade, architecture that won't survive 2× traffic, and eng teams that look senior on LinkedIn but can't ship.

This checklist is what I use when doing independent engineering due diligence for VCs and acquirers ($1,250 fixed one-off, one to two weeks).

## Architecture & scalability

- Can the current architecture handle 2× and 10× users without a rewrite?
- Monolith vs. services: was the split intentional or accidental?
- What is the actual bottleneck — database, queue, third-party API, or frontend?

## Code health signals

- Test coverage: meaningful tests or coverage theater?
- Deployment frequency and rollback story
- Dependency risk (unmaintained packages, license issues)
- Incident patterns in the last 90 days

## Team depth

- Bus factor on critical systems
- Seniority mix vs. what the pitch deck claims
- Hiring plan realism (can they actually close the roles?)

## AI / ML claims (often skipped)

- Production AI vs. demo-ware: evals, fallbacks, cost controls?
- Model routing and token spend — is there a bill shock coming?
- Data pipelines: where does training/inference data actually come from?

## Security baseline

- Secrets handling, auth model, dependency vulnerabilities
- Compliance gaps that block enterprise sales

## Deliverable investors actually use

Not a 200-page PDF. A traffic-light summary per area, specific findings with evidence, and recommended questions for management.

## Links

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