Technical Due Diligence
Investment-grade code assessment in days, not months.
Due Diligence That Moves at Deal Speed
You’re evaluating a software acquisition. The LOI has a timeline. Your deal team needs a technical risk assessment before the next board meeting. And the Big 4 firm you called wants six weeks and a six-figure retainer.
That math doesn’t work for most deals. Especially in the mid-market — the $5M-$100M software acquisitions where technical risk is real but Big 4 budgets aren’t justified.
We built our technical due diligence practice for this exact gap. Agent-accelerated analysis paired with senior engineer judgment. Investment-grade reporting your committee can act on. Delivered in days, not months.
Why Technical DD Has Changed
The software you’re evaluating isn’t what it was two years ago. Forty-one percent of all code on GitHub is now AI-generated. A quarter of Y Combinator’s Winter 2025 batch was built with 95% or more AI-generated code. The tools that made this possible — Cursor, Bolt.new, Lovable, Copilot — produce code that looks professional on the surface and can be structurally hollow underneath.
Traditional technical due diligence wasn’t designed for this. A checklist that asks “is there test coverage?” gets a “yes” — because AI tools generate tests. But those tests often don’t catch real bugs. A checklist that asks “is the architecture sound?” gets a “yes” — because the code is organized into files and folders. But there’s no actual separation of concerns.
The correlation between how code looks and how code works has broken. Surface-level DD gives you false confidence. Your deal team needs something deeper.
We’ve written extensively about what technical due diligence should cover and the specific risks of AI-generated codebases. This page covers how we do it — and why we can do it faster than anyone else.
How We Work: Human + Agent
Our process combines automated agent analysis with senior engineer review. The agent handles the mechanical work that would take a human team days. The engineers focus on the judgment calls that agents can’t make.
Day 0 — Engagement and access. NDA execution. Repository access provisioned (read-only, time-limited tokens, named individuals). Data room access for infrastructure docs, incident history, and team information. Kickoff call to understand deal context and focus areas.
Day 1 — Agent analysis. Our automated pipeline runs a comprehensive first pass across the entire codebase. Static analysis across 30+ metrics. Dependency scanning with license compliance and vulnerability detection. AI-generated code pattern detection — identifying Cursor, Bolt.new, Lovable, and Copilot fingerprints. Architecture mapping. Test coverage analysis. Security vulnerability scanning. Infrastructure and deployment assessment. This produces a structured findings report that would take a human reviewer two to three days. The agent does it in hours, with deeper coverage — it reads every file, traces every dependency, checks every endpoint.
Days 2-4 — Senior engineer review. This is where the real value lives. Our engineers take the agent’s findings and apply judgment:
- Reviewing flagged architecture patterns in context. Is this coupling intentional and appropriate, or is it a liability?
- Tracing critical business paths manually. Payment flows. Authentication chains. Data mutation paths. The places where bugs have dollar signs attached.
- Evaluating design decisions against the company’s stage and trajectory. Technical debt that’s normal for a seed-stage startup is alarming for a Series B company.
- Assessing the team and process. Commit patterns, review history, documentation quality, bus factor.
- Quantifying remediation cost. Not “it needs work” — specific engineering-weeks and dollar ranges for each category of findings.
- Writing the executive summary. The 2-page version your investment committee reads to make the go/no-go decision.
Day 5 — Delivery and walkthrough. Report delivered. Live walkthrough with your deal team. Questions answered. Findings contextualized for your specific deal structure and integration plan.
What the Agent Catches That Humans Miss
Time pressure is the enemy of thorough due diligence. A human reviewer with one week to assess a 200,000-line codebase makes trade-offs. They sample. They skim. They focus on the areas they think matter most and hope nothing critical lives in the files they skipped.
Our agent doesn’t skip files. It analyzes every line of code, every dependency, every configuration file. It runs the same comprehensive analysis on a 10,000-line MVP and a 500,000-line enterprise platform.
Patterns the agent detects that manual review typically misses:
- Duplicated business logic across files — a hallmark of AI-generated code where the same rule is implemented differently in six places
- Silent error swallowing — try/catch blocks that catch everything and do nothing, making failures invisible
- Dependency chain vulnerabilities — not just direct dependencies, but transitive dependencies three levels deep with known CVEs
- License contamination — copyleft-licensed code buried in the dependency tree that could affect IP transfer
- Inconsistent security patterns — authentication enforced in some routes but not others, input validation present in some controllers but missing in adjacent ones
- Dead code and abandoned features — code paths that are never executed but still maintained, inflating the apparent size and complexity of the codebase
What Humans Catch That Agents Miss
Agents are pattern matchers. They’re excellent at finding known issues. They’re terrible at evaluating unknown risks. That’s why the human layer isn’t optional.
Architecture appropriateness. An agent can tell you the codebase is a monolith. A senior engineer tells you whether a monolith is the right choice for this company at this stage — or whether it’s a scaling liability that will cost $300K to refactor.
Business logic correctness. An agent can flag a complex function. An engineer who understands fintech tells you the payment reconciliation logic has a rounding error that will compound into material discrepancies at scale.
Integration cost estimation. Only an engineer who’s done post-acquisition integrations can credibly estimate what it takes to bring an acquired codebase into your stack. Agents provide data. Engineers provide estimates your CFO can model.
Team and process assessment. Code tells you what was built. Commit history, PR reviews, and documentation tell you how it was built — and whether the team can maintain it. This requires human judgment about organizational capability.
The “so what” translation. Your investment committee doesn’t need to know about N+1 queries. They need to know that database performance will degrade at 10x current load, remediation costs $80K-$120K, and it should be priced into the deal. Translating technical findings into deal language is a human skill.
What You Get
Executive summary (2 pages). Written for your investment committee. The overall risk assessment, the three most critical findings, remediation cost summary, and a clear recommendation. This is the document that informs the go/no-go decision.
Technical findings report. Every finding categorized by severity, with specific code locations, reproduction context, and remediation guidance. This is the document your integration engineering team uses post-close.
Risk matrix. Findings plotted by severity, likelihood, and remediation effort. Your deal team uses this to decide which risks they accept, which they price into the deal, and which are dealbreakers.
Remediation cost estimate. Engineering-weeks and dollar ranges for each finding category, with confidence intervals. “Authentication rewrite: 4-6 engineering-weeks, $40K-$70K” — not “the auth system needs improvement.”
Integration timeline. How many engineers, for how long, to bring the codebase to your standards post-close. Broken into immediate (month 1), short-term (months 1-3), medium-term (months 3-6), and long-term phases.
AI code assessment. If the codebase includes AI-generated code, we provide a specific assessment: what percentage is AI-generated, which tools were used, whether there was human oversight, and the remediation cost to bring it to production grade. We detail our approach to evaluating AI-built codebases in depth.
Sample Report Structure
This is a condensed view of what your deal team receives. Every engagement is scoped to the deal, but the structure is consistent — your committee sees the same format every time.
Who This Is For
Growth equity and PE firms evaluating software acquisitions in the $5M-$100M range. You need investment-grade diligence without Big 4 timelines or budgets.
VCs doing pre-investment diligence. You need to validate that the technology behind a pitch deck is real before writing a check. Especially relevant when the startup was built with AI tools.
Corporate M&A teams. You’re acquiring a product to integrate into your platform. You need to know the integration cost before you finalize the offer.
Portfolio companies assessing bolt-on acquisitions. Your portfolio company found an acquisition target. You need an independent technical assessment before approving the deal.
Acqui-hire evaluations. You’re buying the team as much as the product. You need to know whether the code is an asset that accelerates the team or a liability they’ll spend months unwinding.
What Happens After the Report
If the codebase is solid, you close with confidence. The report gives your deal team defensible data points for the investment memo.
If it needs work, every finding has a price tag. Use it to adjust the offer, structure earnouts tied to remediation milestones, or negotiate seller-funded remediation before close.
If the findings point to remediation, we can help there too. Our code audit practice handles the detailed technical assessment, and our vibe code cleanup and technical debt services handle the engineering work. We’re the only DD provider that also fixes what it finds — which means your post-close remediation starts with a team that already knows the codebase.
The full process — from pre-acquisition code review through remediation — is a single engagement. No handoffs. No re-learning. No surprises.
Start a Due Diligence Engagement
Deal timelines don’t wait. Neither do we.
Get in touch with the deal context — what you’re evaluating, the timeline, and what your committee needs. We’ll scope the engagement and get started as soon as access is provisioned.