The way due diligence firms assess technology has changed more in the last three years than in the previous twenty. The reason is straightforward: technology has become the primary value driver in most acquisition targets and the traditional approach to tech assessment, a few conversations with the CTO, a review of the architecture deck and a paragraph in the DD report, no longer matches the stakes.
In 2026, when a PE fund pays a premium for a SaaS company, they’re paying primarily for the technology platform. When a strategic acquirer buys an AI-powered product company, they’re paying for the AI capability. When an ARC evaluates a distressed tech asset, they’re evaluating whether the technology has standalone value. In all three scenarios, the technology assessment needs to be as rigorous as the financial assessment and the firms that do this well have developed methodologies that look nothing like the technology reviews of five years ago.
Here’s how the top technical due diligence companies evaluate tech stacks today and why the approach has changed.
From opinion-based to evidence-based assessment
The biggest methodological shift in technical due diligence is the move from qualitative opinion to quantitative evidence.
The traditional approach relied heavily on expert judgment, a senior technologist reviewed the architecture, talked to the engineering team and provided an assessment based on their experience. This approach has two problems that become acute when technology is the primary value driver in a deal.
Expert judgment varies between experts. Two equally qualified assessors can examine the same codebase and reach different conclusions about its quality, scalability and maintainability. When the assessment informs a $50M acquisition price, that variability is a problem.
Expert judgment is difficult to defend in an investment committee. “Our technical advisor says the code quality is adequate” is an opinion. “The maintainability index is 48 against an industry benchmark of 65 and remediation to an acceptable level requires approximately 600 engineering hours at an estimated cost of $120K” is a finding that investment committee members can evaluate and price.
The firms doing this well in 2026 use automated analysis tools as the foundation, static code analysis for quality metrics, dependency scanning for vulnerability assessment, coverage analysis for test distribution and infrastructure cost modelling for cloud economics. The expert judgment layer interprets these metrics in the context of the specific deal thesis, a below-benchmark maintainability score means something different for a company you’re planning to maintain than for a company you’re planning to rebuild.
The six assessment areas that matter
Through working with DD firms across dozens of engagements, I’ve found that the methodology has converged around six assessment areas that together predict the deal outcome with remarkable consistency.
Code quality and technical debt assessment uses automated metrics, cyclomatic complexity, code duplication, dependency age, maintainability index, as the quantitative foundation, supplemented by manual review of the highest-risk modules. The output isn’t a quality score. It’s a remediation estimate: how much engineering time and cost is needed to bring the codebase to the standard the acquirer’s growth plan requires.
The specific metric that top firms have learned to examine carefully is test coverage distribution. The aggregate coverage number is nearly always misleading. An 80% headline that masks 12% coverage on the payment processing module is a finding that changes the risk profile. The distribution analysis, which modules are well-tested and which aren’t, maps directly to the operational risk the acquirer inherits.
Architecture and scalability assessment ties the technical evaluation directly to the deal thesis. If the financial model assumes 3x growth over two years, the architecture assessment tests whether the platform can support that growth. Not theoretically, through actual load testing results if they exist, or through architectural analysis that identifies specific bottlenecks and estimates the remediation required to achieve the target scale.
The question that distinguishes rigorous DD firms from superficial ones: “show me the load test results at your projected growth target.” If the results exist and show clean performance, the growth thesis is validated. If they exist and show degradation, you’ve identified a specific infrastructure investment. If they don’t exist, the growth assumption is untested. All three answers are useful. The question is whether anyone asks.
Security posture assessment goes beyond checking for compliance certifications. The firms that do this well examine the actual production environment, not the policies that describe what the security posture should be, but the evidence of what it actually is. Penetration test results, dependency vulnerability scans, secrets management practices, access control architecture and critically, the scope of any compliance certifications relative to the production environment.
The pattern that catches deal teams: SOC 2 certification that covers corporate IT but not the production SaaS platform. The certification exists and looks reassuring. The coverage doesn’t extend to the system the acquirer is actually buying. Checking the audit scope documentation against the production environment boundaries is a basic step that many assessments miss.
IP and data assessment has become more important as AI capability has become a primary value driver. For AI-powered companies, the assessment examines whether the data that trains the models is owned, licensed, or accessed through partnerships. Whether model training pipelines are reproducible or dependent on specific individuals. Whether the AI capability is genuinely proprietary or is a configuration layer on top of a foundation model. And whether IP registrations name the corporate entity or individuals.
For non-AI companies, the IP assessment focuses on code ownership, employment agreements, contractor assignments, open-source license compliance and data handling practices that create compliance exposure, particularly for cross-border transactions where GDPR and CCPA obligations transfer with the deal.
Cloud economics assessment has become a standard DD component as cloud infrastructure costs have become material operating expenses. The assessment models the infrastructure cost at the target growth levels, comparing the cost-per-unit-of-business-activity at current scale against the cost at projected scale. When the architecture isn’t efficient at scale, the cost per unit increases as volume grows, which means the margin assumptions in the financial model may be wrong.
Team and knowledge assessment maps the distribution of critical knowledge across the engineering team and evaluates the operational risk of key-person departures. Git contribution analysis shows who actually built and maintains each system. Documentation assessment reveals what’s recorded versus what exists only as institutional memory. The output is a retention strategy recommendation and a knowledge transfer plan that should be part of the integration planning.
How findings connect to deal economics
The methodological advancement that separates the best DD firms from the rest isn’t the assessment itself, it’s the translation of technical findings into deal economics.
Every technical finding should map to one of three deal impacts: a price adjustment (the remediation cost changes the economics), a risk item (the finding creates exposure that needs to be managed through deal structure, escrow, indemnity, earn-out adjustments), or an integration planning item (the finding affects how the post-close integration should be sequenced and resourced).
The firms that do this translation well produce reports that investment committees can act on. Not technical documents that need an engineer to interpret, but findings tied to specific financial impacts with estimated costs, timelines and risk probabilities.
This translation skill is what makes technical DD valuable to the deal process rather than merely informative. A finding that “the codebase has high technical debt” is an observation. A finding that “remediating the technical debt to support the growth plan requires 800 engineering hours over four months at an estimated cost of $160K, which should be reflected in the integration budget and delays the feature development timeline by one quarter” is a pricing input.
The AI-specific assessment layer
The newest evolution in technical DD methodology is the AI-specific assessment that’s become necessary as AI capability drives more deal valuations.
When an acquisition target’s value proposition depends on AI, the assessment needs to evaluate dimensions that traditional tech DD doesn’t cover. Model validation methodology, can the company prove its AI works through rigorous out-of-sample testing, not just cherry-picked demos? Data provenance and ownership, does the company own the data that creates its competitive advantage, or is it licensed or accessed through relationships that could change? Inference cost economics, does the AI’s operating cost support the margin assumptions at scale? Foundation model dependency, is the AI capability genuinely proprietary, or is it a prompt-engineering layer on a third-party model that any competitor could replicate?
These questions have moved from niche concerns to standard assessment items as AI-powered acquisitions have become mainstream. The DD firms that added AI assessment capability early have a meaningful advantage in the current market, because the AI-specific findings frequently surface the most material deal risks.
What’s changed and what hasn’t
The tools have changed. Automated analysis provides quantitative foundations that expert-only assessments couldn’t deliver. The scope has expanded to include cloud economics, AI-specific assessment and data provenance. The translation from technical findings to deal economics has become more rigorous and more standardised.
What hasn’t changed is the fundamental purpose: giving the deal team an accurate picture of what the technology actually is, so the price reflects reality and the integration plan is built on verified assumptions rather than hopeful ones. The firms that do this well, regardless of their specific methodology, share a commitment to independent, evidence-based assessment that serves the deal team’s decision-making rather than confirming a thesis that’s already been decided.
That commitment is what makes the assessment valuable. Everything else is methodology.
