As AI coding tools accelerate software development, investors face a new problem during technology acquisitions: understanding exactly what they are buying is becoming harder. Plan A Technologies has acquired a stealth AI assessment tool to strengthen its technical M&A due diligence practice, using automated analysis to examine large, fragmented technology environments and surface risks that traditional reviews may overlook.
For years, technical due diligence in software acquisitions has followed a relatively familiar playbook: interview the engineering team, inspect the architecture, review source code and documentation, scan for vulnerabilities, and assess whether the platform can scale.
Artificial intelligence is complicating that process.
Companies can now produce and modify large amounts of code with AI assistance, potentially increasing development velocity while making it harder for executives, investors and even engineering teams to understand how every part of a technology estate was constructed.
That creates a new diligence question for private equity firms and strategic acquirers: How do you assess the quality and risk of software when the volume and complexity of the underlying technology are growing faster than traditional review processes can handle?
Plan A Technologies is responding by acquiring a stealth AI assessment tool and incorporating it into its technical M&A due diligence practice.
The company says the technology will provide an additional proprietary analysis layer for evaluating technology-intensive transactions. Plan A works with private equity firms, strategic buyers, enterprise companies and private investors to assess the technology underlying potential acquisitions.
The goal is not to replace engineers with an automated scanner. Instead, Plan A is positioning the tool as an analytical system that can help senior technical specialists examine more information, identify patterns and anomalies, and connect technical findings to the financial and operational implications of an acquisition.
AI Is Changing What “Technical Debt” Looks Like
The timing of the acquisition reflects a broader shift in software engineering.
AI coding assistants and autonomous coding agents can now generate, modify and review software at a scale that was difficult to achieve through traditional development workflows. Google Cloud’s 2025 DORA research found that 90% of surveyed technology professionals were using AI at work, while more than 80% reported productivity improvements. At the same time, 30% said they had little or no trust in AI-generated code.
The result is a growing gap between code production and code comprehension.
A development organization may have significantly more software than it did previously, but not necessarily more engineers who understand every architectural decision, dependency or accumulated technical liability.
Gartner has specifically warned that AI coding agents can contribute to architectural technical debt, creating problems that affect application stability, agility and the ability to implement future changes. The research firm has also highlighted new quality and security risks associated with AI-generated code.
For M&A investors, those risks have direct economic consequences.
Technical debt can translate into higher post-acquisition engineering costs. A poorly understood architecture can slow integration. Hidden security weaknesses can create remediation expenses. Fragile systems can require modernization sooner than management forecasts suggest.
The difference between ordinary technical debt and structural technology risk can therefore change the valuation and investment thesis of a transaction.
From Code Review to Technology Intelligence
Plan A’s newly acquired system is designed to help analyze particularly large and fragmented technology environments.
Traditional diligence remains part of the company’s methodology, including expert interviews, architecture reviews, documentation analysis, manual code examination and commercially available scanning tools.
The AI assessment layer is intended to complement those methods by connecting signals across different parts of the technology environment.
That could be particularly useful when a target company has accumulated multiple applications, repositories, infrastructure environments, databases and third-party systems through years of organic growth or previous acquisitions.
Rather than examining those components as isolated assets, an AI-assisted assessment can potentially help identify relationships and recurring patterns across the broader technology estate.
The distinction is important. A vulnerability scanner might identify a specific security issue. An architecture review might identify a scaling limitation. A senior engineer might recognize excessive technical debt.
A broader assessment system can potentially help connect those observations into a larger picture of how the technology actually works and what it could cost to change.
That is the type of analysis Plan A says it wants to strengthen.
Why Human Judgment Still Matters
The company is taking a deliberately cautious position on automation.
Plan A co-founder Slav Kulik says AI should not substitute for senior technical judgment. Instead, the company’s thesis is that AI can make experienced engineers and architects more effective by allowing them to process larger amounts of information and focus attention on the issues that require human interpretation.
That distinction may become increasingly important as AI enters enterprise engineering.
A model can identify an unusual dependency or architectural pattern, but determining whether that pattern is a material business risk requires context. An apparently outdated component might be harmless in one system and critical in another. A technically complex platform may be expensive to modernize but strategically valuable because of proprietary workflows or data.
Technical due diligence therefore remains an exercise in judgment, not simply detection.
Plan A’s approach is closer to AI-assisted technology intelligence than autonomous auditing.
M&A Is Becoming More Technology-Centric
The opportunity extends beyond software companies.
As technology becomes embedded in healthcare, financial services, manufacturing, retail and other industries, software architecture increasingly becomes part of the value proposition being acquired.
McKinsey’s 2026 Global Private Equity Report argues that AI is changing how dealmakers underwrite both risk and value, with greater emphasis on asset-specific technology capabilities, data advantages, competitive moats and execution potential. The report also notes that AI is already being used in sourcing, diligence and portfolio monitoring, although adoption remains uneven.
That makes technology diligence increasingly connected to operational value creation.
Plan A’s model is notable because the company also builds software, AI agents, data platforms and cloud infrastructure. Its engineers can therefore assess not only whether a target has a problem, but what it might take to fix it.
That can change the purpose of a diligence report.
Instead of ending with a list of weaknesses, an acquirer can potentially leave the process with a modernization roadmap: which systems need attention, which risks are material, what investments are likely to be required, and which initiatives should happen first.
A New Role for AI in the Deal Process
The acquisition reflects an emerging category of enterprise AI applications that is less visible than generative AI copilots but potentially important for investors: AI systems that turn complex technical environments into decision intelligence.
For M&A teams, that means AI may increasingly be used not only to summarize contracts and financial documents but to examine the technology itself.
Plan A says the tool’s creator, Serhii Melnychuk, has joined the company as an architect and will lead its integration and future development.
The broader strategy is equally significant. Rather than simply buying an external software license, Plan A is building a proprietary layer into its own engineering and advisory practice.
If AI-generated software continues to increase the volume and complexity of enterprise technology, that type of tooling could become a competitive advantage in technical diligence.
The central challenge for investors will remain the same: determining what a technology asset is really worth.
What is changing is how much technical evidence needs to be examined to answer that question.
Market Landscape
AI-assisted technical due diligence sits at the intersection of M&A advisory, software engineering, cybersecurity, application modernization and enterprise AI.
Traditional technical due diligence providers rely heavily on engineering interviews, architecture reviews, code analysis and security assessments. Automated code-scanning and software-composition tools add machine-driven analysis, but typically focus on narrower technical questions.
Plan A is positioning its proprietary AI assessment layer around the broader technology estate and its implications for acquisition risk.
The timing is significant. Gartner’s 2026 research identifies AI-driven technical debt, code quality and security as emerging concerns for software engineering organizations.
Meanwhile, private equity firms are incorporating technology and AI more deeply into investment underwriting and portfolio value creation. McKinsey reports that leading firms are increasingly evaluating AI upside and downside directly in diligence and investment planning.
The likely competitive landscape will include specialist technical diligence firms, cybersecurity platforms, application intelligence providers and increasingly AI-native assessment tools.
Top Insights
- Plan A is adding proprietary AI analysis to technical M&A diligence as AI-generated software makes technology estates larger and harder to assess manually.
- The tool is designed to surface patterns and anomalies across fragmented systems while complementing, rather than replacing, senior engineering judgment.
- AI-driven technical debt is becoming an important acquisition risk because hidden architectural problems can increase post-close modernization costs.
- Private equity firms are increasingly evaluating technology quality, data advantages and AI capabilities as part of investment underwriting.
- Plan A’s build-and-assess model could allow diligence findings to translate directly into post-acquisition modernization and engineering roadmaps.
