Product Case · Data & AI Product Strategy
This is a Product Strategy 0–1 statement.
Self-directed portfolio project (2026), not a live enterprise deployment. Metrics and targets on this page are illustrative, built to show product judgment, not reported as measured results.
HR couldn't see attrition risk until it showed up in an exit interview. Legal couldn't see contract bottlenecks or missed obligations until someone asked. Both ran on manual, spreadsheet-stitched reporting from systems that were never built to talk to each other. I designed one governed semantic layer serving both functions — with lineage, provenance, and a review-lane model so AI outputs get flagged or suggested, never decided unsupervised.
Attrition intervention prototypeHR couldn't see attrition risk or skills gaps until they showed up in an exit interview; Legal couldn't see contract bottlenecks until someone asked.
Every report was rebuilt from scratch by hand, stitched from systems never designed to talk to each other, with no lineage back to source.
One semantic layer, governed once, serving two functions — not two disconnected dashboards built on four different exports.
Figure 1 — shared semantic layer serving two modules through common governance.
Two models sit on top of the semantic layer — an attrition-risk score for HR, and a contract/compliance anomaly flag for Legal. Neither acts unsupervised.
Routine compliance updates and standard renewal reminders — logged with a full audit trail.
Attrition risk flagged to the HRBP, an unusual clause surfaced to counsel — one-click accept, edit, or dismiss.
A potential compliance breach or high-value anomaly, routed directly to a named owner.
Figure 2 — every AI output routes to auto-apply, suggest, or escalate, based on confidence and stakes.
Every AI output is flagged, suggested, or escalated — never decided unsupervised.
Chosen to answer the question every stakeholder actually asks — is this worth what it costs — rather than vanity usage numbers. Illustrative targets, not measured results.