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Technical Due Diligence

Know what you're buying before you buy it.

For investors, acquirers, and boards assessing technical risk in a target company.

What we assess

Codebase quality
Architecture patterns, test coverage, dependency health, and maintainability scoring.
AI architecture
Model selection rationale, training data practices, evaluation rigour, and serving infrastructure.
Security posture
Auth, secrets management, dependency vulnerabilities, and PII handling.
Scalability
Database design, bottleneck identification, and infrastructure headroom assessment.
Technical debt
Quantified debt load, categorised by severity and estimated remediation effort.
Team capability
Engineering team structure, knowledge concentration risk, and hiring gaps.

Our process

01
NDA + data room access
We sign an NDA, access code repositories, infrastructure docs, and architecture diagrams via your data room.
Day 0
02
5-day deep assessment
Our engineers conduct a structured review across all assessment areas simultaneously.
Days 1–5
03
Draft report + management review
We share findings with you for factual review before the report goes to the requester.
Day 6
04
Final report delivery
Complete written report delivered to the commissioning party, with an optional verbal debrief.
Day 7

The report

A structured, written document covering:

Executive summary
Risk matrix
Code quality score
AI architecture review
Security findings
Recommended remediations
Deal recommendations

Who uses this

Series A/B investors pre-term sheet
Validate technical claims in a pitch before committing capital. Know the real state of the codebase before the deal closes.
PE firms pre-acquisition
Identify technical debt, key-person risk, and AI architecture weaknesses that affect valuation and integration complexity.
Founders preparing for fundraise
Understand your own technical risk profile before investors find it for you. Fix what you can, and present honestly what you can't.

Speed & confidentiality

Turnaround
Standard: 5 business days
Full report delivered within one week of data room access. Suitable for most deal timelines.
Rush: 72 hours
Available for time-sensitive deals. Covers all the same areas with additional resource allocation. Rush pricing applies.
Confidentiality
Mutual NDA signed before any access is granted
All reviewers sign individual confidentiality agreements
Access is read-only - no code is modified
Data room access revoked immediately on completion
Report delivered only to the commissioning party

Common findings we surface

Anonymised examples from past engagements:

Undocumented AI models
Core prediction models with no architecture documentation, no version control, and no reproducible training pipeline. Replacement effort estimated at 6–9 engineer-months.
No test coverage
0% automated test coverage across production services. Every release is a manual regression - risk of silent regressions increases with team growth.
PII in training data
Customer email addresses and names present in model training datasets without consent documentation. Potential GDPR and CCPA exposure.
Vendor lock-in risk
Critical inference logic coupled to a single cloud AI provider with no fallback. Provider deprecation or pricing change would require significant re-engineering.
Full scope

Our due diligence checklist

Code & Architecture
✓Repository structure and modularisation
✓Dependency audit (CVE scan + outdated packages)
✓Test coverage measurement (unit, integration, e2e)
✓CI/CD pipeline completeness and reliability
✓Code review process and merge controls
✓Hardcoded secrets or credentials audit
AI & ML Systems
✓Model versioning and reproducibility
✓Training data provenance and consent documentation
✓Evaluation rigor and benchmark validity
✓Model monitoring and drift detection presence
✓Inference infrastructure and serving latency
✓Bias and fairness assessment documentation
Security Posture
✓Authentication and authorization patterns
✓Secrets management (vaults, env hygiene)
✓PII handling and data residency compliance
✓API security and rate limiting
✓Third-party vendor security review
✓Incident response procedures
Scalability & Operations
✓Database schema design and query performance
✓Infrastructure-as-code coverage
✓Single points of failure identification
✓Backup and disaster recovery documentation
✓Observability (logging, metrics, alerting)
✓Key-person risk and bus factor analysis
Risk categories

How we classify risk

Critical

Deal-breaking or liability-creating issues that must be resolved before close or flagged as significant valuation risk.

Examples
–PII in training data without consent
–No auth on API endpoints
–Critical system with single engineer knowledge
High

Significant issues that affect scalability, security, or maintainability. Remediation effort should be factored into valuation.

Examples
–Zero test coverage
–Vendor lock-in on critical path
–No observability or alerting
Medium

Technical debt or architectural patterns that will slow down engineering velocity but don't create immediate risk.

Examples
–Outdated dependencies
–Monolith blocking parallel feature work
–Manual deployment processes
Low

Best-practice gaps that are worth addressing in a post-close roadmap but don't affect the deal.

Examples
–Missing API documentation
–Inconsistent code style
–Partial CI coverage
Informational

Observations that don't constitute risk but inform post-acquisition planning or hiring strategy.

Examples
–Stack familiarity of acquiring team
–Architectural patterns chosen
–Framework version strategy
Track record
5 days
Standard turnaround from data room access to full report
72hrs
Rush review available for time-critical deal timelines
Proven
Technical due diligence reviews completed across SaaS, AI, and fintech
100%
Of reviews delivered under NDA with individual reviewer agreements
Investor feedback
"StartxLabs identified three critical findings our internal team missed - including a GDPR exposure in the training data. It saved us from a significant post-acquisition liability."
General Partner - Tier 1 European Venture Fund

Related

Industries we apply this to

AI for SaaS

In-app AI copilots, semantic search, and multi-tenant AI pipelines for B2B SaaS.

Fintech AI

SOC 2/PCI DSS compliant AI for fraud detection, credit risk scoring, and KYC automation.

Healthcare AI

HIPAA-compliant AI for clinical decision support, diagnostic imaging, and EHR integration.

Ready to build your
next digital product?

Whether you have a detailed specification or just an early idea - we'll help you scope it, challenge the assumptions, and deliver it on time. No pitch decks. Straight to the point.

Get in TouchSee Our Work

What happens next

1

Send us a message

Tell us what you're building or what's broken.

2

Discovery call (30 min)

We ask hard questions. You get honest answers.

3

Scoped proposal

Clear deliverables, timeline, and team in 48 hours.

Contact Us

Tell us about
your project

Whether you have a detailed brief or just an early idea, we will help you scope it, challenge it, and ship it.

  • Agentic AI development and multi-agent systems
  • Generative AI consulting and LLM integration
  • RAG development and custom model deployment
  • Data engineering, MLOps and custom software
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