Enterprise RAG Application Suite
Three production retrieval-augmented-generation applications giving non-technical staff natural-language access to enterprise data and decision support.
Business and engineering teams at a Fortune-class logistics enterprise needed answers from large internal data and document stores, but getting them required technical intermediaries — analysts, queries, manual searches — which slowed every decision cycle.
Architected and launched three flagship RAG applications: a natural-language data query tool, an internal knowledge assistant, and an automated RFP/document processor. Built retrieval grounding, guardrails, evaluation, and operational monitoring in from the start. Drove the blueprint from POC through a funded production rollout by translating the GenAI case into executive business terms.
Three applications shipped to production within 18 months. Secured executive funding by converting the GenAI blueprint into a concrete, costed business case.
The decision to make reliability infrastructure non-negotiable from day one — retrieval grounding, evaluation harnesses, operational monitoring — is what separated these applications from internal demos that never reach production. Non-technical users can only trust a system if it visibly fails safe; that means knowing when the model doesn’t know, surfacing source attribution, and alerting on distribution shift.
Securing executive funding required a different kind of architecture work: translating an 18-month technical roadmap into a business case with a payback narrative. The POC phase was as much about building internal credibility as proving the technology.
The three applications cover different RAG patterns — structured data querying, unstructured document retrieval, and hybrid extraction for RFP response — which made the suite a practical reference architecture for subsequent LLM initiatives across the enterprise.