Capability
AI Product Engineering
The part that decides whether any of it reaches production
The unglamorous half: the backend, the data path, the deployment, the controls and the measurement. Most AI work that fails does not fail at the model — it fails here.
What we build
- Backend architecture and AI APIs built for real traffic
- Data pipelines feeding retrieval and generation
- Deployment, gating and rollback that a team can operate
- Monitoring, spend controls and the switches to turn a feature off
What is hard about it
A demo and a product differ by everything except the demo
Auth, tenancy, retention, cost ceilings, rate limits, audit, a rollback that works, and a way to turn the expensive path off without a deploy. None of it appears in a prototype and all of it decides whether the prototype becomes a system.
Measure the workflow, not the model
Model benchmarks answer a question nobody in the business asked. What matters is whether the work got done, how often a person had to intervene, and what it cost — and those have to be instrumented deliberately, because nothing reports them by default.
Assume it will be wrong in production
Generation costs money per call and fails in ways tests do not reproduce. Budgets per day and per visitor, a switch that disables generation without a deploy, and logs that say what was actually sent are not operational polish; they are what makes the feature safe to leave running.
Proven in
Runs in production behind per-day and per-visitor render budgets, a deploy gate, and controls that switch generation off without a release.
A unified inventory layer serving two different front doors — filtered search and a conversational assistant — from one source of truth.