If you are waiting four weeks for a Big 4 consultancy to return a 100-page red-flag report, you have already lost the deal—or worse, you are about to overpay for it. In the current high-velocity market, the traditional technology due diligence playbook is broken. It is too slow, too academic, and fundamentally disconnected from the investment thesis.
Here is the reality facing Private Equity Operating Partners today: 76% of technology acquisitions fail to meet their financial objectives, according to McKinsey data. Why? Because the standard diligence checklist focuses on inventory (what they have), not velocity (how fast they can move) or debt (what it costs to fix).
Most PE sponsors look at a target's engineering organization as a black box. You see the inputs (OpEx, headcount) and the outputs (product features, revenue). But you don't see the friction inside. You don't see that 30% of their R&D budget is being burned on paying down interest on technical debt rather than shipping new features.
We recently audited a $50M SaaS target that looked pristine on paper. Their margins were healthy; their growth was steady. But a 5-day deep dive revealed that their core platform relied on a deprecated framework that would reach end-of-life in 18 months. The cost to replatform? $4M. That wasn't an operational expense; that was a purchase price adjustment waiting to happen. If the deal team had relied on a standard high-level architectural review, they would have missed a CAPEX bomb equivalent to 10% of the deal value.
You do not need weeks to assess technical health. You need 5 days of targeted, operator-led scrutiny. This framework is designed to move from "gut feel" to empirical data, converting technical risk into financial terms.
Before you interview a single engineer, let the code speak. Request access to their repositories (GitHub/GitLab) and run a Software Composition Analysis (SCA). According to Synopsys' 2024 OSSRA report, 74% of commercial codebases contain high-risk open source vulnerabilities. You are looking for:
Forget the PowerPoint diagrams. Look at the cloud bills and monitoring logs. Does their AWS spend scale linearly with users (bad) or logarithmically (good)? We use a simple "User-to-Cost" ratio. If adding 1,000 users increases infrastructure costs by 15%, the architecture is monolithic and unoptimized. A modern, microservices-based architecture should see marginal costs decrease at scale.
Who is actually writing the code? In many founder-led firms, 80% of the critical code is written by two people—often the Founder/CTO and one lead architect. If they leave, the IP leaves. We analyze commit logs to determine the "Bus Factor" (how many people need to get hit by a bus for the project to stall). If the Bus Factor is 1, you have a massive key-person risk that requires a retention earnout.
Can they ship software? Ask for their "Cycle Time"—the time it takes for a line of code to go from a developer's laptop to production. Elite performers do this in hours. Low performers take weeks. If their deployment process is manual, fragile, and prone to rollbacks, your Value Creation Plan (VCP) for rapid product expansion is dead on arrival. You will spend the first year building plumbing, not features.
This is where we translate fluent DevOps into fluent EBITDA. We categorize findings into three buckets:
The output of a technical due diligence should not be a list of bugs. It should be a CAPEX Bridge. If we identify $2M of necessary remediation to support your growth thesis, that is $2M that should come off the purchase price or be structured as a holdback.
We worked with a PE sponsor looking at a logistics platform. Our 5-day assessment found that while the front end was modern, the database schema was hard-coded for a specific customer type. To expand into new verticals (the investment thesis), the entire data layer needed a rewrite. We quantified this as a 6-month, $1.5M delay. The sponsor used our data to negotiate a $2.5M reduction in the purchase price and added a specific milestone-based earnout for the CTO to deliver the new schema.
By Friday afternoon of the assessment week, you should be able to answer three questions with absolute certainty:
Speed is your edge, but clarity is your weapon. Don't buy black boxes. Buy transparent, scalable engines. If you can't measure engineering efficiency in the first week, you won't be able to manage it for the next five years.
