Enterprise Data & Insight Platform
A team-built platform that unifies signals from several upstream data systems and in-house capture layers into one queryable record, used for monitoring, risk flagging, benchmarking, and AI-assisted insight generation.
I contributed to this platform rather than building it — roughly 40 commits against a codebase of more than 1,300. I'm including it because the interesting experience is operating inside a large, multi-team AI and data system, and because the integration boundary between it and Everstream is one I worked directly on.
- 01Multiple upstream systems unified
- 02In-house capture layers integrated
- 03One queryable record per subject
- 04Monitoring and risk flagging
- 05AI-assisted insight generation
What it integrates
Signals arrive from several upstream measurement, scheduling, and assessment systems alongside in-house capture layers, and are reconciled into a single record per subject. The specific systems and capture layers are the client's architecture rather than mine, so they aren't detailed here.
The architecture pass
The part I find most instructive is how the canonical data model was introduced: a small set of primitives defined as strictly union-typed interfaces, reached through adapters over the tables that already existed rather than through new ones. Nothing was required to consume them on day one, so the pass shipped non-breaking into a live platform.
- Canonical primitives reached through adapters over existing tables
- Strict union-typed enums rather than free-form strings
- Stable identity with cross-system id mapping and merge handling
- Non-breaking rollout into a platform already in production