Technical Due Diligence is far more than a formal inspection of a digital asset. In substance, it is an examination of the product’s actual viability, its technological reserve, and its capacity to withstand future operational pressure. Its significance becomes especially visible in situations where the cost of error is prohibitively high. This applies, first of all, to M&A transactions, when a corporation acquires a startup in order to strengthen its own market position. It also applies to product-synergy scenarios, where, even before rebranding, it is necessary to determine whether an external solution can be embedded into a joint product, platform, or ecosystem subscription. The same logic is relevant before a new investment round, when existing investors and senior management must present the project in a transparent and convincing manner. Finally, Technical Due Diligence is equally vital for operational audits, particularly when identifying the root causes of underperforming IT teams and outlining a realistic roadmap for remediating development processes..
Such an audit is built as a multi-layered architectural analysis. It usually covers business architecture, solution design, the platform layer, and infrastructure. Yet decomposition into layers, by itself, does not guarantee that the intended result will be achieved. The more important question is whether each of these levels corresponds to the declared, or sometimes only discovered during the audit, non-functional requirements. This is the point at which presentation promises are tested against technological reality. Reliability, performance, security, and scalability are not abstract virtues. They are measurable criteria that make it possible to determine in advance whether a system can survive at corporate scale or whether it will begin to fracture under the first serious increase in load.
The first element under scrutiny is almost always technical debt. At the early stages of product development, teams quite often exchange architectural quality for speed of market entry. Such a trade-off may be justified, at least for a time. But only up to a certain limit. Later, the accumulated compromises turn into a risk factor: architectural defects, excessive coupling between components, weak testability, and fragmented engineering decisions begin to slow product growth and make integration painful. For this reason, the purpose of the audit is not simply to record deficiencies. It is to determine their scale, the cost of remediation, and their impact on subsequent operation. At the same time, the flexibility of the architecture itself is assessed. If the solution is built on autonomous services, for instance with the use of Spring Boot, adaptation to the corporate standards of a holding company is usually less difficult. If, however, the system rests on a monolithic core, the price of change rises, while the speed of transformation declines.
No less important is the audit of the data landscape and storage systems. Here, the focus is not on a single database, but on the entire set of data stores of different classes and types. The analysis covers both traditional SQL and NoSQL solutions, including PostgreSQL and MongoDB, as well as the internal logic of data structures, table schemas, and transaction-isolation mechanisms. Caches and queues require separate attention: Redis, Memcached, RabbitMQ, and Kafka often conceal performance problems and bottlenecks that are not immediately visible from the outside. Object storage, S3-compatible services, file systems, and approaches to handling configurations, secrets, and access keys in Vault or KMS also fall within the scope of the audit. The maturity of the analytical layer becomes particularly important. The condition of the Data Lake, DWH, and log-collection systems makes it possible to understand how manageable the environment is and whether its data can be trusted. At the same time, the audit evaluates not only the storage systems themselves, but also backup and disaster-recovery procedures, backup encryption, and access-segregation rules. It is often in these seemingly routine details that the most dangerous vulnerabilities are found.
A separate layer of verification concerns the clarity with which the system is divided into modules and services. In modern distributed architecture, it is not enough simply to declare a solution “microservice-based.” The real question is whether the boundaries between components have been drawn meaningfully. Expert review shows whether approaches such as Domain-Driven Design were used to identify contexts, define bounded contexts, and assign areas of responsibility, or whether the system was split into parts spontaneously, without methodological discipline. In the latter case, a well-known and rather hazardous construction emerges: the distributed monolith. Externally, such a system appears modular, yet in practice it remains tightly coupled. Proper isolation, by contrast, means that changes in one segment do not trigger a chain reaction of failures elsewhere, and that engineering teams can develop their areas independently rather than blocking one another at every step.
The analysis of data transport between components is also of substantial importance. This is exactly where risks tend to hide—risks that are rarely visible at the level of a presentation but immediately manifest themselves in production. In microservice systems, the quality of synchronous communication through REST or gRPC is assessed: whether retry mechanisms exist, whether circuit breakers are implemented, and how prepared the system is for failures and degradation of external dependencies. The correctness of distributed transaction design is examined as well, including the use of the Saga pattern and the presence of mechanisms for compensating or rolling back states when errors occur. Message brokers and event-driven architecture deserve particular attention. The use of RabbitMQ, Kafka, and similar tools must be technologically justified, not merely fashionable. If Kafka is effectively being used as a simple queue in a context where strict processing order is required, this becomes a warning sign.
The maturity of the engineering team is especially visible through the state of the SDLC. A well-organized software development life cycle is not a decorative add-on. It is evidence that the product is being managed systematically. The presence of automated CI/CD pipelines indicates more predictable releases and reduces dependence on manual operations. Still, this alone is insufficient if a full testing culture has not been established. For that reason, the audit necessarily covers test documentation, the relevance of test cases, the presence of realistic load profiles for stress testing, and the depth of API coverage by automated tests. Where testing exists only nominally, the perceived stability of the product becomes an illusion, and each production release turns into a high-stakes experiment.
Information security and the assessment of Open Source risks occupy a central place in Technical Due Diligence. Reviewing DevSecOps practices makes it possible to determine whether security is built into the development process or remains an external, delayed control. The system is analyzed for vulnerabilities; encryption algorithms are assessed; the protection of interservice communication is examined; and access-management practices are checked for correctness. The layer of external dependencies may be just as dangerous. Open Source components accelerate development, but they also create vulnerable parts of the solution if they are not continuously monitored. Therefore, known CVEs are checked, unsupported libraries are identified, and outdated dependencies are tracked. Such dependencies may quietly accumulate long-term risks and then become the source of critical problems at the worst possible moment.
Finally, the audit cannot be complete without analyzing the technology team itself. To assess the project’s dependence on individual employees, the history of commits in the VCS is examined, allowing the so-called Truck Factor to be calculated. This approach reveals the true knowledge distribution inside the team, not merely the formal version shown in an organizational chart. If critical technical knowledge is concentrated in the hands of only a few developers, the business faces a substantial key-person risk, where the departure of a single specialist could effectively paralyze further product evolution. Conversely, a more even distribution of authorship and competence indicates a mature engineering culture and greater project resilience.
Ultimately, Technical Due Diligence moves the evaluation of a startup from the realm of intuitive expectations into the domain of precise calculation. As a result of such an expert review, not only a map of technological development is formed, but also a realistic IT budget for the first year after the transaction. This is why a thorough audit of infrastructure, data transport, architectural logic, and development processes is not a bureaucratic formality. It is an instrument for protecting capital. It allows the buyer or investor to recognize, in time, where there is a genuinely scalable solution and where there is only an outwardly attractive product concealing costly and unpredictable rework.
