RAG Knowledge System
75% Faster Support Resolution in 6 Weeks
The Challenge
A mid-market healthtech platform was drowning in 12,000+ articles across three legacy systems. Agents spent 18 minutes per ticket locating the right document.
Our Approach
We built an abstraction layer that ingests, chunks, and embeds content from all three sources into Pinecone with metadata-aware filtering and a reranking pass tuned to clinical domain intent.
The Execution
Delivered across 6 weeks with the following technology stack:
How we worked
Discovery
Deep-dive into existing systems, constraints, and stakeholder interviews.
Architecture
Design the system blueprint, data models, and integration points.
Prototype
Ship a working slice end-to-end to validate assumptions.
Build
Full development with weekly demos and continuous integration.
Deploy
Production rollout with monitoring, rollback plans, and training.
Scale
Performance tuning, documentation, and knowledge transfer.
The Results
- 75% faster resolution time (18 min → under 5 min)
- 89% first-query accuracy (up from 34%)
- 40% of tickets deflected before reaching human agents
Architecture Overview
The Future
This engagement established a foundation we continue to build on. The systems we shipped are now handling production workloads, and the architecture we designed is positioned for the next phase of scale.
Want to see RAG Knowledge System in action?
Watch the live demoGenorah turned our fragmented knowledge base into a system that actually thinks. Support tickets that took 20 minutes now resolve in under 5.