Enterprise AI Copilot (Internal Prototype)
Prototype proving AI tooling could bridge product and engineering at scale — first agent fully operational
Enterprise retail teams running AI-powered shelf monitoring had intelligence spread across multiple domains (pricing, promotions, assortment, compliance, forecasting, etc.) but no unified way to query across them. Analysts bounced between dashboards and spreadsheets. There was a gap between product teams who understood the problems and engineering teams who could build solutions—and no tooling to bridge it.
Mapped the existing AI platform’s domain architecture to understand capabilities and gaps. Identified that the core challenge wasn’t building another chatbot—it was proving that AI tooling could route questions to the right domain context and generate actionable answers. Built a prototype query router and tested it against 14 real business questions to validate the concept.
Designed a multi-agent copilot framework spanning 17 retail intelligence domains. Built out the routing layer, data pipeline, and first fully operational agent with structured evidence retrieval from PostgreSQL. Mock mode enables demos without live database access. Artifact generator creates formatted reports for executive review. Deployed on Railway with health monitoring via Grafana dashboards.
- Agent routing over single-model RAG — domain-specific agents with tailored system prompts produce dramatically better answers than generic retrieval
- Mock mode as a first-class feature — enabled executive demos weeks before live data integration was complete
- FastAPI over Flask — needed async endpoints for concurrent agent calls and streaming responses
- Railway deployment over internal hosting — gave stakeholders a URL they could share without VPN or setup
- Architected 17-domain agent framework with routing layer, data pipeline, and first agent fully operational
- Demo’d working prototype to VP Product and company president—validated the concept for further investment
- 13,000+ lines of production Python with full data layer, agent registry, and governance spec
- Mock mode handles 14+ question categories for demos without live database dependency
- Deployed on Railway with health monitoring, Grafana integration, and Perplexity AI search