Know what you're buying, before the price is locked in.
Independent technical due diligence for deal teams. I read the code, assess the team, and test the AI claims. You get a costed verdict your investment committee can act on.
Scoped to the stage the deal is at
Four ways in, depending on how far along you are: a quick screen before you commit resource, a targeted audit of the AI story, a full review for signing, or an ongoing technical voice for the portfolio. Every engagement is fixed fee, agreed in writing before any work starts.
AI Usage Audit
Nearly every target now claims AI makes its engineering faster, or that AI is the product. Very few buyers can test either claim. I look at what AI actually shipped, whether anyone reviewed it, the debt accumulating underneath, and how much of the moat is a thin layer over someone else's model.
Red Flag Screen
A fast early check before you go too far into a deal, or just want a second opinion on a codebase. Flags anything that could be a serious problem.
Full Technical Due Diligence
The full review for deals approaching signing. Everything below, costed and prioritised, in a report written to be read by an investment committee rather than translated for one.
Technical Advisor, On Call
A fixed block of hours each month for a fund, a board, or a portfolio company that wants an experienced engineering voice on call, without re-scoping an engagement every time a question comes up.
Hands-on Senior Engineering Leader
I'm David Goodman. I've spent 20+ years in software engineering, the last 9 running teams of up to 50 across fintech, cybersecurity and enterprise SaaS, from American Express through to fast-moving scale-ups and startups.
You're not hiring a faceless firm running a generic checklist. You're hiring someone who has built the systems and led the teams he's now being asked to judge, and who will be the one sitting in your investment committee call explaining what he found.
Goodman Diligence is a new practice. The engineering career behind it isn't. It exists to put 20+ years of hands-on technical judgement to work for deal teams who want a direct, experienced read, not a template report from a firm that has never shipped code.
I've led AI transformation at organisation scale: rolling out Claude Code and agentic development workflows, automated PR reviews, and coding standards across whole engineering, data engineering and data science functions, rather than talking about it in a slide deck. That's why I can tell the difference between a target using AI well and one saying it does. My first degree was in Artificial Intelligence, back in 2006, with an MSc in Information Technology since.
Connect on LinkedInEvery angle I actually check
Findings are not left as opinions. Each one comes with a severity, an estimate of what it costs to fix and how long it takes, and whether it is a pre-close condition or a year-one budget line, so it can go straight into the model or the price conversation.
Code quality
Is it well written, easy to change safely, properly tested, and not full of duplication.
Technical debt
The shortcuts that were taken, and what they'll actually cost to unwind.
AI
Code shipped by AI tools without proper review, and whether the team is actually using AI well, or just says it is.
Architecture
Whether the system, and the data behind it, is built to grow or just built to survive today.
Security
How secrets and access are handled, and what happens if something goes wrong.
Team
How the team is set up, and how dependent it is on any one person.
Four steps, start to report
Get in touch
Company, stage, and timeline.
Agree scope
Fixed before anything starts.
Review
Codebase, team, and systems.
Report + call
A clear verdict, walked through.
Scope, fee and delivery date are fixed in writing before any work begins, so there is no open-ended meter running and nothing lands after your deadline. Every engagement is covered by NDA. Happy to sign yours, or use mine. Codebase and company data are reviewed under strict confidentiality and never retained beyond the engagement.
Built for decisions where being wrong is expensive
The fee is a rounding error against the deal. Finding out after completion is not.
Private Equity
Costed findings you can take into the price discussion, put in the SPA, or use to walk away. Buy-side or vendor-side, mid-market deals and bolt-ons.
Venture Capital
Find out whether the engineering story, and the AI story in particular, matches the repository before the term sheet becomes a wire.
Corporate Acquirers
Catch what is expensive to fix after close, and understand what integrating it into your own estate will really take, while it is still the seller's problem.
Founders Preparing to Raise or Sell
See what a buyer's diligence will find, while there is still time to fix it or frame it, rather than during the process.
Got a deal on the clock, or one coming up?
Based in the UK, working on deals wherever they are. Replies within one business day, and happy to sign an NDA before you tell me anything about the target.