How we build

Because prompts and models change, and without evals every change is a vibe check on production. An eval suite — real cases from your domain, scored automatically — turns "did the update break anything" into a test run instead of a support-ticket surprise. It is the single practice that most separates AI systems that survive their first year from demos that got deployed.

A named person approves AI output before it commits — sends, publishes, files, pays — wherever the blast radius justifies it. Concretely: queues with approve/edit/reject, not automatic sends; audit logs of what was approved by whom; and gate placement decided by consequence, not convenience. Gates can loosen later with evidence; launching loose and tightening after an incident is the industry's standard regret.

Minimum necessary, in your accounts wherever possible: models called with the least context that does the job, secrets in your vault not our laptops, no training on your data by us ever, and provider data-handling settings configured deliberately rather than by default. Data-flow documentation is part of every scope, because "where does our data go" deserves a diagram, not a shrug.