AI decision-support platform
Analytics discovery platform that grew from a searchable hub to an AI diagnostic engine
Summary
Team members spent hours searching across dashboards and tools to find metrics and build trust in data. Built an AI decision-support platform unifying metrics, dashboards, and operational docs in one conversational interface with row-level access control. Shipped with modular, LLM-agnostic retrieval, visualization, and answer pipelines. 199 people asked 1,838 questions; 79 recurring users across 16 job families; daily active users peaked at 73.
Context
Internal / Enablement, partner-facing (Kroger, Aeon, Casino)
What I built
Built an AI decision-support platform unifying dashboards, docs, and tools behind one hub. It grew from a searchable discovery hub into a diagnostic engine that routes questions, retrieves relevant data, generates answers, and visualises results in one flow. Shipped modular tools (Metric Tree for root-cause diagnosis, Cost to Serve modelling, Tote Mix Optimiser) with direct partner feedback loops. Over Mar-Aug 2026: 199 people asked 1,838 questions; 79 recurring users across 16 job families; daily active users peaked at 73.
Impact
- ●199 people asked at least one question (Mar-Aug 2026)
- ●1,838 questions total; 79 recurring users (2+ distinct weeks)
- ●Daily active users peaked at 73; reached 16 job families
- ●Shipped Metric Tree, Cost to Serve, Tote Mix Optimiser modules with direct partner feedback iteration