Product
AI Product Engineering
Ship production AI features without spending six months assembling a specialist team.
Add production AI to a product you already ship, or build an AI-native app from the ground up.
System shape
How we approach it
We design the AI surface around a clear user job, build the full path (UI, API, model), harden with evals, and hand off something your team can own.
Where this creates value
Jobs this outcome is hired to do.
- 01AI features in existing SaaS products
- 02AI-native applications from architecture to launch
- 03APIs, data pipelines, and full-stack delivery
- 04Handoff your team can own and extend
What engineering includes
The build work behind the outcome.
- AI architecture and system design
- Agents, retrieval, and model integration
- Backend, frontend, and data pipelines
- Evaluation and production hardening
- Documentation and knowledge transfer
How delivery works
From first map to something live you can measure.
01
Define the thin slice
One user job, one data path, one success metric before expanding scope.
02
Architect & build
UI, API, and model path shipping in short cycles with reviewable increments.
03
Harden for users
Evals, fallbacks, rate limits, and security basics before broad exposure.
04
Handoff
Docs, ownership map, and a path for your team to extend the system.
Example use cases
Concrete jobs this outcome is built for. Not attributed client claims.
01
AI feature in existing SaaS
Add draft, chat, or recommend flows to a product you already ship, with production auth and APIs.
02
AI-native first release
Build a focused first version real users can try, structured so it can harden instead of being rewritten.
03
Internal operator product
Give ops or support a UI around agents or retrieval so the team can run the system daily.
What you walk away with
Typical deliverables for a scoped engagement.
Technical depth: Full-stack AI products
Have a use case for this outcome?
Bring the workflow or product need. We will say whether a sprint, pilot, or a different path fits.