Product & Technical Due Diligence

Architecture, technical debt, scalability, security, data, AI and machine learning risks, the engineering team, build-versus-buy choices and intellectual property ownership.

Can it scale, is it secure, and who owns it?

Lessons

• Ask what breaks, and what it costs, at ten times the customers — Technical diligence tests whether a product can serve far more customers without collapsing or destroying margins, by looking at architecture, infrastructure costs and past incidents. (11 min)
• Technical debt shows up as time engineers cannot spend on new work — Technical debt is the accumulated cost of shortcuts in a product's code and systems. Diligence measures it through how much engineering time goes to maintenance, how fast changes ship and how often they break things. (10 min)
• A breach can cost a young company its customers, not just its data — Security diligence checks how a company protects systems and customer data, what incidents it has had, and whether it meets the obligations its customers and regulators expect. (10 min)
• An AI product's margin and moat depend on what it does not own — Companies built on AI models carry specific risks: dependence on model providers, inference costs that grow with usage, rights to training data and outputs that are hard to evaluate. Diligence tests each. (11 min)
• If one engineer leaving would stop the product, that is the risk — Technical diligence on people looks at how knowledge is concentrated, how the team hires and makes decisions, and whether technical leadership matches the company's next stage. (9 min)
• The company must own its code and follow the licences it uses — Technical diligence confirms that everyone who wrote the product assigned their rights to the company, and that open-source components are used in ways their licences allow. (10 min)

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