Design, build, and scale AI-powered products.

A traditional Product team is a PM, a few engineers, and a Designer. An AI product adds data scientists, ML engineers, and evaluation specialists to the mix, each optimizing for different metrics and speaking a different language.
The PM arbitrates between model accuracy, user experience, infrastructure cost, and Time-to-Market all at once. This goes beyond adding a new member to the squad: it's a different collaboration model entirely.

Traditional product prioritization weighs impact against effort. AI product prioritization adds a layer: is a 3% improvement in model accuracy worth three weeks of engineering time, knowing those same three weeks could go to improving the UX, enriching the training data, or cutting inference costs?
The backlog covers model performance, user experience, and infrastructure at once. Most product frameworks don't account for this – you need new ones.

While a traditional product ships and stabilizes in maintenance mode, an AI product ships and immediately starts drifting: the data the model was trained on becomes stale, and performance degrades without anyone changing a line of code.
User feedback feeds directly into model retraining and eval datasets, improving the product as it runs. Model quality evaluation is a permanent practice, and cost optimization (from model selection to inference trade-offs) is an ongoing Product decision.