Diligence That Answers the Investment Question
Most technical due diligence tells you the state of the code. Stride tells you whether the thesis holds, where the advantage actually lives, and what we could not verify.
Why Most Technical Diligence Misses the Question
A conventional code scan returns scores. It tells you the code is half covered, that dependencies are aging, that complexity is concentrated in three modules. What it does not tell you is whether the claim your price rests on is real. Buyers end up with a document full of findings and still have to guess at the one thing they were trying to learn: does the thesis hold, and does it survive the deal? A scan tells you the code is half covered. We tell you which sliver is holding up the valuation.
AI-powered technical due diligence is a thesis-first assessment of a target's technology. Agents read the engineering record exhaustively, across every service rather than a sample, while experienced engineers run the interviews, apply operator judgment, and write the conclusions. The output is a plain answer to the investment question, not an inventory.
What Makes Stride's Technical Due Diligence Different
We start from the thesis, not an inventory. Every engagement begins with the claim that sets the price: a proprietary technical advantage, a platform that scales, an AI capability competitors cannot match. We work backward from that claim to the evidence, so the report answers the question you are actually underwriting rather than cataloguing everything we found along the way.
We find what scanners miss. Key-person risk that a tool reports as clean. Architecture that looks modern in the repository and behaves like a monolith in production. The distance between what has shipped and what lives on a roadmap slide. These surface in the engineering record and in the interviews, not in a static analysis pass.
We connect code to the P&L, and we say what we could not verify. Technical findings are translated into the numbers financial diligence is already working with: cost to replicate, cost to remediate, cost to integrate, and how long a funded competitor would need to catch up. Where the evidence did not support a conclusion, we name the gap instead of filling it with a score.
Striders build and ship production AI systems for a living, and have done so since 2014. We assess code and whole platforms through the lens of both the CTO and the CFO, which is why our conclusions read like an operator's judgment rather than a checklist.
What a Stride Technical Due Diligence Engagement Looks Like
Every engagement is shaped around the deal in front of us, sequenced by the claim the price rests on and the time available before the decision. Across that work, the same activities recur. These are the components of a Stride diligence engagement.
Start From the Thesis
We open with the claim that sets the valuation and turn it into questions evidence can answer. A proprietary advantage, a platform that scales, an AI capability a competitor cannot match: each becomes a specific thing to verify in the code, the delivery record, and the team.
Exhaustive Analysis, Not Sampling
Agents read the engineering data across every service: source, commit and release history, backlog, incidents, dependencies, and infrastructure. Coverage is exhaustive rather than a sample, so the finding that matters is not the one that happened to fall outside the window.
Engineer-Led Interviews
Our engineers run the conversations with the target's technical leadership and the people building the product. Engineers who have shipped production systems know which answers to press on, and what a confident answer sounds like when the thing behind it does not exist yet.
Product Versus Roadmap
Releases and backlog get read against the story being told. We separate what is shipped and running from what is planned, prototyped, or demonstrated, and we say which part of the narrative sits on each side of that line.
Key-Person and Concentration Risk
We map who actually holds the system in their head, how much of the critical path runs through them, and what replacing them would cost in time and money. This is the risk automated tooling most often reports as clean.
The Written Answer
You get a plain conclusion: whether the thesis holds, where the advantage actually lives, how long a funded competitor would need to replicate it, what remediation and integration would cost, and what we could not verify.
What This Looked Like on a Live Deal
The Claim
A private equity buyer was under LOI on a software company whose valuation rested on a single claim: a proprietary technical advantage. A conventional code scan had already been run. It returned scores, and it did not answer the question that set the price.
What We Read
500,000 lines of code. 2,345 backlog tickets. 30 services. 1,818 production releases. Every service analyzed rather than sampled, with our engineers running the interviews alongside.
The Answer
Where the advantage actually lived, how long a funded competitor would need to replicate it, and which parts of the story were roadmap rather than product.
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Frequently Asked Questions About Technical Due Diligence
What is AI-powered technical due diligence?
It is a thesis-first technical assessment of an acquisition target. Agents read the engineering record exhaustively, across every service rather than a sample, while Stride engineers run the interviews, apply operator judgment, and write the conclusions. The AI provides coverage no manual review could reach inside a deal timeline. The judgment about what it means stays human.
How is this different from an automated code scan?
A scan tells you the state of the code. It returns coverage percentages, complexity scores, dependency warnings, and a findings list. None of that answers whether the claim your price rests on is real. We start from the thesis and work back to the evidence, which is why the report ends in a conclusion rather than a score.
What do you actually deliver?
A written answer to the investment question, with the evidence behind it: whether the technical claim holds, where the advantage actually lives, how long a funded competitor would need to replicate it, what remediation and integration would cost, who holds critical knowledge and what replacing them would take, and what we could not verify.
What does it mean that you say what you could not verify?
Diligence reports often paper over gaps with a hedge or a score. When access, time, or evidence did not support a conclusion, we name the gap and say what it would take to close it. A buyer can price a known unknown. False confidence is what gets repriced after close.
Where do we start?
A conversation about the deal and the claim that sets the price. From there we scope the engagement against your timeline, confirm what access we need to the codebase, the delivery record, and the team, and agree on the question the report has to answer.