Technical diligence at seed stage is a different exercise than at Series A or B. At later stages, you are evaluating a system that exists at scale: production infrastructure, data pipelines with defined throughput, security controls that have been tested against real attack surfaces. At seed, the product is often weeks away from the demo, the infrastructure is a single cloud instance with default configurations, and the training data pipeline is a Python script on the founder's laptop. The question is not whether the current system is production-ready — it is not — but whether the team has the depth to build something production-ready, and whether the current system reflects genuine technical choices or accumulated accidents. Answering that question requires a different kind of inquiry.
The first thing we look at in a technical diligence session is the founding team's decision narrative. We ask them to walk us through a technical decision they made in the last three months that they later changed. The question is not about the specific decision — it is about whether the team can articulate what they learned, why the first approach was wrong, and what the second approach costs them in technical debt. Founders who cannot answer this question with specificity either have not made real technical decisions yet (the product is more concept than code) or they are not the kind of reflective engineers who build systems that survive contact with production reality. Teams who answer with honest detail about a mistake they made and the tradeoff they accepted to fix it are the ones we want to work with.
For AI-native products specifically, the model architecture conversation tells us a great deal. We ask: what is the inference latency target for your core workflow, and how does your current model architecture perform against it? How are you handling model degradation when input data distribution shifts away from your training set? What does your labelling pipeline look like, and what is your current cost per labelled example? These questions do not have universally correct answers — the right answers depend on the specific application — but a team that has genuinely thought about them will have answers. A team that has bolted a pre-trained transformer onto their product without understanding its performance characteristics under their specific usage distribution will not. The gap between these two teams is the gap between a product that will survive scale and one that will require a complete rebuild at Series A.
Data access and proprietary data strategy is the dimension we evaluate most carefully. The defensibility of most AI-native B2B products over a three to five year horizon depends on their ability to accumulate training data that is difficult for competitors to replicate. This requires a clear plan for how deployment generates labelled data, how that data is structured and stored, and who owns it contractually. It is common for early-stage founders not to have thought through the data ownership terms in their customer contracts — sometimes the customer has inserted terms that give them ownership of any models trained on their data. This is a fixable problem at seed stage but not at Series B. We read the customer contract template in diligence, specifically looking for the data ownership and model training clauses.
We are not looking for founders who have already solved every technical challenge in their product. We are looking for founders who have correctly identified which technical challenges are the hard ones, and who have built their current architecture to give them the most information possible about whether those challenges are solvable. A team that has shipped a working demo that falls apart under even modest load has taught us less about their capability than a team that has shipped a working demo alongside a clear analysis of why the current architecture cannot scale and a credible plan for what they need to build next. The former is impressive-looking; the latter is what engineering maturity actually looks like at pre-product-market-fit stage.