Orion — End-to-end AI product delivery
Taking an AI product from a working prototype to a delivered, self-hosted system — integration, orchestration, and the unglamorous work that makes it reliable.
Architecture
The problem
Orion had a working proof-of-concept and a real customer problem, but the gap between “the demo works” and “this runs every day for a client” was the whole job. Integrations that fail in ways the happy path never shows. Agents that go down the wrong branch. Output that looks right but isn’t.
Approach
I owned the delivery end-to-end: wiring the prototype to real data sources and APIs, turning a script into an orchestrated system with retries and error handling, and getting it running self-hosted so the client keeps control of their own data. The bulk of the work wasn’t the model — it was everything around it that determines whether the system is trustworthy in production.
What I learned
Delivery work is mostly the second half of the project. The prototype is the easy 20%; the delivery is the 80% where the actual value — and the actual risk — lives. The part of the job that matters is sitting next to a real customer problem and doing whatever it takes to turn it into a running system they can rely on.
Specifics of this engagement (exact integrations, metrics, timeline) are client-confidential and summarized here; details available on request.