Agentic Decision-Intelligence Platform
Private commercial engagementA commercial agentic decision-support platform I built as sole developer, from canonical data model to interface.
- 2,807 graded checks
- 151 browser assertions
- Sole developer
Stack
- Python
- React + TypeScript
- REST APIs
- Canonical data model
- Multi-agent workflows
- Graded acceptance suite
A commercial agentic decision-intelligence platform, built as the sole developer across the whole stack: Python services and domain logic, a React and TypeScript interface, the APIs between them, and a canonical data model that everything else resolved against. Requirements moved throughout, so the architecture had to absorb change without the data model drifting underneath it.
The reasoning layer is source-grounded by design. Values carry their provenance and their uncertainty all the way to the interface instead of being flattened into a single confident answer, multi-agent workflows do the work of assembling and cross-checking, and a human approves anything consequential. The system is built to be questioned: a user can always ask where a number came from and get a real answer.
Release was gated on evidence, not on a demo. Acceptance ran as 22 independently graded families over a 2,807-check graded suite, plus 151 end-to-end browser assertions against the running application, and a longitudinal verification pass of 750 record-field checks that confirmed no factual value regressed and no provenance was lost between releases.
This was client work under a commercial engagement, and it is not my IP. The client, their data, the schemas, and the implementation stay confidential; what is described here is the engineering I owned.