
Where AI fits in a website or mobile product
A practical way to decide which product tasks benefit from AI, which should remain deterministic, and how to connect experimentation to a responsible delivery plan.
Practical thinking for teams building or operating digital products across product, engineering, AI, design, and software delivery.

A practical way to decide which product tasks benefit from AI, which should remain deterministic, and how to connect experimentation to a responsible delivery plan.

The product decisions behind a document workflow that handles varied inputs, uncertain extraction, review, and the boundaries between automation and human correction.

A workflow perspective on combining repository, design, and delivery signals while keeping review context, limitations, and human responsibility visible.

A decision guide for comparing an internal team, external product partner, dedicated capacity, and a bounded build around scope, ownership, pace, and continuity.

A grounded look at the roles, records, data boundaries, integrations, review states, and responsibilities that shape financial-product software.

A practical framework for designing SaaS interfaces around user goals, information, permissions, states, exceptions, and the work people need to complete.

A way to evaluate AI ideas by task, input, output, consequence, review, fallback, cost, and operational ownership instead of starting with a model.

A practical approach to choosing the first workflow, reducing assumptions, defining evidence, and creating a responsible path from MVP to the next product decision.