How to Assess Technical Debt Before an Acquisition

In most transactions, technical debt is not hidden. It simply never gets counted. Management refers to it in general terms as something that will need attention eventually, the data room says nothing about it, and the fund signs on the assumption that if the product works and customers are paying, the technology will somehow carry the growth plan. The bill arrives later, usually in the first year after close, when it turns out that every new feature costs twice what the model assumed and half the product budget goes to fixing what was inherited.

Technical debt is one of the few risks that can be measured before signing, and yet it is routinely skipped. Only around 15 percent of private equity deals include a dedicated technical due diligence, which means that in most cases the core of the business is taken on trust. What follows is a practical account of how to assess technical debt before an acquisition: what it actually is from an investor's point of view, where to look for it, how to turn engineering observations into a number that reaches the model, and what a properly run assessment looks like when it has to stand up in front of an investment committee.

What Technical Debt Means to an Investor

In engineering conversations, technical debt is often reduced to code quality. For an investor that definition is too narrow and actively misleading. In a deal context, technical debt is the gap between how the system is built today and how it would need to be built to carry the investment thesis. If the plan assumes doubling the customer base, entering three new markets and integrating with partners, then the debt is everything standing in the way of that plan: architecture that cannot scale horizontally, module boundaries too blurred to expose clean APIs, release processes that slow delivery, security gaps that close the door on enterprise buyers.

An important consequence follows. The same codebase can be heavily indebted under one investment thesis and entirely adequate under another. A system that has reliably processed hundreds of thousands of transactions a year for a decade is a proven asset if the plan is steady organic growth, and a serious constraint if the plan is aggressive expansion and rapid feature release. Assessing debt in isolation from the thesis is therefore an academic exercise. The question that matters is what this particular plan demands from the technology and what it costs to close the gap.

It is also worth separating debt from neglect. Every system that has run long enough to build value accumulates debt, and that fact alone is not a red flag. The red flag is the absence of awareness: when the team cannot say where the debt sits, what it costs, and what happens if nothing is done about it.

Why the Debt Has to Be Priced Before Signing

Sequence has a direct financial consequence here. Debt identified before signing becomes part of the negotiation: a price adjustment, a change to the multiple, a term in the agreement, or a line in the value creation plan that the fund knowingly funds. The same debt discovered six months after close is simply an unplanned cost, absorbing the product budget and delaying the thesis.

There is a second, less obvious reason. Pricing the debt before the transaction changes how the first hundred days are planned. A fund that walks in with a prioritised remediation list and an estimate of the effort involved starts creating value on day one. A fund that starts by discovering loses the first quarter to diagnosis, and often the second to convincing the team that the diagnosis is right. That difference is frequently worth more than the assessment itself.

Eight Areas Where the Debt Actually Sits

A rigorous assessment of technical debt is not a matter of reading the code and issuing a grade. It means working systematically through the areas where debt genuinely affects the company's ability to execute the plan. In the assessments Altimi runs there are eight of them, and each answers a different investor question.

• Architecture and stack. Dependency mapping, technical debt quantification, and validation of upgrade paths. This is where end-of-life components surface, along with the places where modules are so tightly coupled that any change touches half the system.
• Code quality and engineering practices. Static analysis, test coverage, CI/CD maturity, and code health scored against industry benchmarks. It answers whether the team can change the system safely or whether every change is a gamble.
• Infrastructure and cloud readiness. Deployment architecture, cost efficiency, scalability ceiling, and vendor lock-in. This is where you see whether the cost of running the platform will grow in line with revenue or faster than it.
• Security and compliance. OWASP risk scanning, authentication patterns, data handling, and regulatory exposure including GDPR, SOC 2, and ISO 27001. Gaps here can close off entire market segments.
• AI and data maturity. AI adoption scoring, data pipeline readiness, model governance, and concrete value creation opportunities. Increasingly this is the area that determines whether the asset compounds or falls behind.
• Scalability and growth scenarios. Load testing results, horizontal scaling feasibility, and cost projections at two, five, and ten times current volume. This is the most direct collision between the technology and the investment thesis.
• Team and delivery maturity. Engineering org structure, delivery velocity, key-person risk, and the credibility of the hiring plan. Debt often sits not in the code but in the fact that only one person understands how billing works.
• The 90-day value creation roadmap. A remediation plan with effort estimates, quick wins, and strategic investments for the period after close. This is where diagnosis turns into a plan of action.

Working through all eight has a further benefit. Debt rarely occurs in isolation. Thin test coverage, an immature release process, and knowledge concentrated in one engineer usually travel together, because they share a cause: pressure to ship at the expense of foundations. Assessing area by area exposes that pattern rather than just the individual symptoms.

Turning Engineering Findings into a Number

The weakest point of most technical assessments is that they stop at description. The report notes that the architecture is tightly coupled and test coverage is low, and leaves the investor asking what that means for the price. An assessment that is useful in a transaction has to go three steps further.

The first step is effort estimation. Every material finding should carry an estimate of the work required to resolve it, expressed in concrete resources and time. Without that, nothing can be entered into the model.

The second step is prioritisation by severity. Not all debt needs to be repaid. Some can be deliberately left in place if it does not conflict with the thesis. Findings are therefore ranked in a risk matrix by severity and urgency, separating what must be fixed before scaling from what can simply be monitored.

The third step is translation into deal language. Quantified debt enters the model in three ways: as a one-off remediation cost in the value creation plan, as elevated run-rate cost in the forecast, and as the risk of delaying the thesis, which feeds through to the multiple. Only in that form does a technical assessment become an argument in the negotiation rather than an appendix nobody reads.

A consistent scoring scale helps considerably. A red, amber, or green rating applied to each area, with a score attached, lets a Partner or Principal take in the whole picture in seconds while a CTO drills into detail where it matters. The same document serves two very different needs.

The Findings That Never Appear in the Data Room

Experience across more than a hundred buyer-side assessments shows that the most expensive findings rarely come from documents. They emerge only once there is access to the code and direct conversation with the engineering team.

In one mature B2B marketplace operating cross-border in Europe for over a decade, the commercial metrics were clean and the platform had proven its resilience over time. It was only the architecture review that showed how organic growth had created tight inter-module dependencies and limited API boundaries. Not unusual for a platform at that stage, but the consequence is concrete: delivery slows as the team grows, and each new external integration becomes harder to execute cleanly. The business had scaled well; the technology needed to catch up with the ambition.

In another case, a cloud platform serving multi-site operators, the product had strong fit, real switching costs, and a stable team. The assessment revealed that critical system knowledge covering incident response, integration logic, and deployment procedures was concentrated in a small number of individuals with no formalised ownership. Common in founder-led companies at that stage, but it changes the post-acquisition risk profile significantly. Continuity has to be engineered, not assumed.

A third example, an established SaaS platform with strong merchant traction across Central and Eastern Europe, looked solid on retention and had a credible product roadmap. Under the hood, dependency governance and versioning across open-source components were not standardised, and CI/CD maturity lagged behind what the platform's scale would suggest. Some modules were in good shape; others carried accumulated debt that was invisible from the outside. Nothing that cannot be addressed, but it has to be modelled into the post-close roadmap before commitments are made.

The common thread is that in each case the business metrics gave no warning. Technical debt does not appear in a revenue report until it starts constraining growth, and by then it is already a cost the buyer has absorbed.

What a Well-Run Assessment Looks Like

The binding constraint in a transaction is time. The window for technical work is narrow and has to fit the process timetable, which means an assessment of technical debt should be designed as a deal workstream rather than a consulting project. In practice a well-run assessment fits into two weeks and moves through four stages.

The first two days cover mandate and scoping: a briefing on the investment thesis, provisioning of access, and definition of priority areas. Days three to eight are AI-augmented analysis, with the codebase, dependencies, security posture, and infrastructure scanned and then reviewed by engineers. Days nine to twelve bring deep-dive sessions with engineering leadership and evaluation of team maturity. The final days produce the report and the investment committee presentation.

Using AI in the analysis phase cuts discovery time by around 60 percent, but it does not replace judgment. Tooling maps dependencies and detects patterns in code far faster than a human can, while the assessment of whether a given finding threatens the investment thesis remains the work of experienced engineers. That distinction is what makes it possible to combine a short timeline with real depth.

The demand on the target company is lighter than most expect: read-only access to code repositories and the cloud console, plus two or three sessions with the CTO or VP of Engineering. All of it takes place under a signed NDA, before any deal context is shared.

What the Assessment Does Not Cover

An honest assessment has clearly drawn boundaries. Technical due diligence focuses on technology, infrastructure, security, and engineering capability. It does not replace financial model validation, market sizing, IP litigation review, founder background checks, or commercial due diligence. The value comes when the technical findings are aligned with the commercial workstream rather than run in parallel without contact. A capable provider will integrate its conclusions with the commercial adviser's work so that the investment committee receives one coherent picture rather than two unconnected documents.

Independence is a separate matter and just as material. An assessment of technical debt is only meaningful if its author has no interest in the debt appearing larger or smaller than it is. That makes the fee structure and the transparency of prior relationships significant: a fixed fee with no follow-on incentives, disclosure of any previous relationship with the target, and declining the mandate outright where a material conflict exists.

How Altimi Approaches It

Altimi delivers buyer-side technical due diligence for private equity, venture capital, and growth investors across Europe. The assessment is delivered at a fixed price within a scope agreed before kickoff and completed in two weeks, producing a full set of materials built for the investment committee: a roughly 50-page report scored red, amber, or green, a risk matrix with a go or no-go recommendation, a scalability and AI maturity assessment, an evaluation of team and delivery maturity, and a 90-day value creation roadmap. An executive presentation for the committee walkthrough and defined revision rounds are included.

The track record covers more than a hundred buyer-side assessments across SaaS, FinTech, HealthTech, and industrial deep tech, backed by over 150 engineering engagements. That second number matters in practice: a team that builds and modernises systems day to day estimates the real cost of remediation differently from one that only audits. Because Altimi as a technology partner combines capability across product and application engineering, DevOps and cloud security, and AI and data enablement, the remediation an assessment recommends can be executed by the same team, which lowers the true cost of acting on the findings.

Independence here is an operating principle rather than a claim. Altimi acts only for the investor, on a fixed fee with no follow-on incentives, discloses any prior relationship with the target, and declines mandates where a material conflict exists. The current scope and price are published on the technology due diligence page.

A Note for European and DACH Funds

For funds operating in the DACH region, Central and Eastern Europe, and the wider European market, assessing technical debt carries an additional regulatory dimension. GDPR exposure, requirements arising from frameworks such as ISO 27001 and SOC 2, and enterprise buyers' security expectations translate directly into the target's addressable market. A gap in this area is not a technical footnote but a constraint on growth that has to be priced.

Where deal data and source code physically reside during the assessment also matters. Working with an EU-based, ISO 27001-certified partner keeps sensitive material inside the European data protection perimeter throughout a confidential process. In cross-border transactions, where the target operates in one country and the fund reports in another, that consideration can be as significant as the findings themselves.

Conclusion

Technical debt is unusual among deal risks in that it is fully quantifiable before signing, and yet it is most often discovered afterwards. An assessment with real value for an investor starts from the investment thesis, works systematically through the eight areas where debt affects the company's ability to grow, and ends not in a description but in a number: an estimated remediation effort, a ranked risk matrix, and a plan for the first ninety days after close.

Every finding surfaced before signing is either a price adjustment in the fund's favour or a line in a plan the fund knowingly funds. Every finding discovered after close is simply a cost. The whole difference comes down to who counted the debt first.

If you have a live deal and want to discuss scope and timing, the fastest way to start is a short conversation about the target in front of you.