Conducting Technical Due Diligence

Structures technology diligence for venture and growth-stage investments, covering architecture review, code quality assessment, scalability analysis, technical debt quantification, and engineering team evaluation.

When To Use

• Pre-term-sheet or post-term-sheet diligence on a software-intensive target company
• Assessing a startup's technical infrastructure before seed, Series A, or later rounds
• Evaluating acqui-hire candidates where engineering talent is the primary asset
• Follow-on investment decisions where product has scaled since initial diligence
• LP or co-investor requests for independent technical assessment

Inputs To Gather

• Architecture artifacts: System diagrams, infrastructure-as-code repos, cloud provider dashboards (AWS/GCP/Azure), CI/CD pipeline configs
• Codebase access: Primary repositories, commit history (12+ months preferred), branch/merge strategy documentation
• Engineering org data: Team roster with tenure, hiring plan, reporting structure, attrition history
• Operational metrics: Uptime/SLA history, incident post-mortems (last 12 months), deploy frequency, mean time to recovery (MTTR)
• Product roadmap: Feature backlog, technical roadmap items vs. product-driven items, deferred maintenance list
• Third-party dependencies: Vendor contracts, open-source license inventory, API integrations list
• Security posture: Penetration test reports, SOC 2 / ISO 27001 status, vulnerability scan results [VERIFY: request NDA-gated reports directly from target]

Workflow

1. Scope the engagement

   • Define diligence depth: lightweight (2–3 day review) vs. comprehensive (1–2 week deep dive)
   • Identify red-line issues specific to the deal thesis (e.g., "Can this platform handle 100x current load?" or "Is the ML model defensible IP?")
   • Establish data room access and NDA coverage for code review

2. Architecture review

   • Map the system topology: monolith vs. microservices, data stores, message queues, external integrations
   • Assess cloud infrastructure design: redundancy, auto-scaling configuration, disaster recovery plan
   • Evaluate data architecture: schema design, data pipeline reliability, storage costs at projected scale
   • Flag single points of failure and vendor lock-in risks

3. Code quality assessment

   • Review commit history for contribution patterns (bus factor), code review discipline, and merge hygiene
   • Analyze static analysis metrics: test coverage percentage, linting compliance, cyclomatic complexity
   • Sample-review critical modules (auth, payments, core business logic) for code clarity and correctness
   • Identify copy-paste duplication, dead code, and abandoned feature branches

4. Scalability analysis

   • Review load testing results or request a load test against projected 12–24 month user growth
   • Evaluate database query performance at scale: indexing strategy, N+1 queries, read/write split architecture
   • Assess whether current infrastructure costs scale linearly or super-linearly with users
   • Identify architectural ceilings that would require a rewrite vs. incremental optimization

5. Technical debt quantification

   • Catalog known debt items from issue trackers, tech-debt tags, and developer interviews
   • Estimate remediation cost in engineer-months for critical items
   • Classify debt as: (a) blocking scale, (b) increasing operational risk, (c) slowing velocity, or (d) cosmetic
   • Compare debt load against company stage — early-stage debt is expected; Series B+ debt warrants scrutiny

6. Engineering team evaluation

   • Assess team composition: seniority distribution, specialization gaps, key-person dependencies
   • Review hiring pipeline and employer brand signals (Glassdoor, GitHub presence, conference talks)
   • Evaluate engineering culture indicators: documentation habits, on-call practices, blameless post-mortems
   • Benchmark compensation against market to assess retention risk [VERIFY: use current comp data for target geography]

7. Security and compliance review

   • Verify authentication/authorization implementation (OAuth, RBAC, token management)
   • Check secrets management practices (no hardcoded credentials, proper vault usage)
   • Review open-source license compliance — flag copyleft licenses in proprietary distribution paths [VERIFY: license obligations depend on distribution model]
   • Assess data privacy controls relative to applicable regulations (GDPR, CCPA, HIPAA if applicable) [VERIFY: regulatory scope depends on target's markets and data types]

Output

Structure the technical diligence report with:

• Executive summary: 1-page investment-grade overview with go/no-go recommendation and confidence level
• Risk matrix: Categorize findings as Critical / High / Medium / Low with estimated remediation effort
• Architecture scorecard: Rate each domain (infrastructure, data, security, scalability, code quality) on a standardized scale (e.g., 1–5)
• Technical debt ledger: Itemized list with severity, estimated fix cost, and business impact if unaddressed
• Team assessment: Strengths, gaps, and key-person risk summary
• Deal-specific answers: Direct responses to the red-line questions defined in scoping
• Recommended post-close actions: Prioritized technical initiatives for the first 90 days if the investment proceeds

Quality Checks

• Every critical finding is supported by specific evidence (code snippets, metrics, configuration references) — no unsupported assertions
• Scalability claims are grounded in load test data or architecture analysis, not founder assertions
• Technical debt estimates use engineer-month units with stated assumptions about team productivity
• Security findings reference specific CWE/OWASP categories where applicable
• The report distinguishes between stage-appropriate trade-offs and genuinely concerning deficiencies
• All jurisdiction-dependent or regulation-dependent findings are marked with [VERIFY]
• The executive summary is understandable by non-technical investment partners