Technical Due Diligence

Scope is matched to the size and complexity of the target and the level of technical risk involved, ranging from a focused audit of the highest-risk domains to a comprehensive review across all of them. We set this out in a written Statement of Work detailing the deliverables, timeline, and cost, agreed with the investor before we begin.

We typically request access to architecture diagrams, technical documentation, code repositories, and deployment procedures, along with interviews with the company's key technical staff.

The work is organized around our standard TDD questionnaire, which we complete collaboratively in a workshop with the company's subject matter experts, with further calls as needed to close gaps, clarify areas of concern, and discuss remediation of any issues we identify. We also research the company using public sources, so the assessment does not rest solely on what the company provides.

We handle all material in confidence, working under an NDA or joinder with the investor and covered by the investor's mutual NDA with the target company. We are also happy to enter into an NDA directly with the target if required.

Most of what we find is manageable and feeds directly into valuation, terms, or a remediation plan. Surfacing and pricing those issues is much of the value of the work.

An outright dealbreaker is less common, since a deal that reaches technical diligence has usually cleared the investor's earlier screening, but it does happen. When it does, we raise it with the investor immediately rather than holding it for the report, so they can decide whether and how to proceed; if they continue, we assess the impact and set out remediation options and estimated costs to inform their negotiation or decision.

Yes. We reviewed our first AI company in 2019.

We evaluate data pipelines, model governance, training and validation practices, reproducibility, and risks related to data privacy and model bias.

Some investors are exploring whether AI tools can substitute for professional TDD. While AI can be a useful aid - we use it to augment our analysis and accelerate mechanics - relying on it alone introduces significant risks:

• Lack of contextual judgment: AI can summarize what it finds but cannot assess whether a technical decision was reasonable given the company's stage, budget, or market context. That requires experience with comparable companies.
• Incomplete information access: AI works with what it is given, and training is uneven across specialized domains. A skilled practitioner knows what to ask for, what gaps in the documentation are suspicious, and when something is being withheld or obscured.
• No ability to interview: A significant part of TDD is reading the technical team - how they respond to questions, what they avoid, and how they describe their own debt and shortcomings. AI cannot do this.
• Surface-level review: AI tools can scan code and documentation for obvious issues but miss architectural decisions whose risks only become apparent when you understand the business model and growth trajectory.
• Hallucination and false confidence: AI tools are unlikely to acknowledge the boundaries of their own knowledge, and may produce authoritative-sounding assessments based on incomplete information, giving investors false confidence in findings that have not been properly validated.

TechScale Partners brings a disciplined, proven methodology to every engagement. Our TDD framework has been refined through years of real-world investment studies, and our findings are grounded in direct assessment, hands-on experience, and professional judgment; not just AI-powered pattern matching against training data.