✅ Quick Summary
92% verified answer accuracy—measured against 1,200 real-world support questions—is what this enterprise chatbot achieved after rebuilding its architecture around advanced retrieval-augmented generation (RAG).
The organization manages tens of thousands of monthly inquiries across web and voice channels. At that scale, even a small accuracy gap compounds quickly: more escalations, higher compliance exposure, and erosion of customer trust. Their previous chat experience delivered fast responses, but inconsistent grounding led to hallucinations and costly human follow-ups—undermining the promise of always-on AI support.
In regulated, high-volume environments, “mostly correct” is not enough. A production-ready customer support AI must be citation-grounded and auditable, benchmarked against real tickets, seamlessly integrated across web and voice, and designed for safe fallback with clear confidence thresholds and human escalation paths.
This case study unpacks how the team re-architected its support stack using multi-stage retrieval, cross-encoder re-ranking, and calibrated confidence scoring—then deployed it through Verly AI as a scalable chatbot across digital channels. The result was not just higher accuracy, but a measurable reduction in escalations and a support system the enterprise could confidently operate at scale.
The Challenge
The chatbot was confidently wrong.
Customers using the company’s existing web chat assistant were receiving fast answers, but too many were unverified, partially outdated, or missing policy nuance. In regulated workflows, that was not a minor UX issue—it was a compliance risk.
At peak volume, the enterprise was handling 40,000+ monthly conversations across web and voice, 1,200 recurring support scenarios with policy dependencies, and more than 300 versioned documents updated quarterly. Escalation rates exceeded 35% from live chat interactions.
The root problem was not the language model. It was retrieval.
The previous chatbot relied on naive vector search over loosely structured documents. There was no version control, no metadata filtering, and no re-ranking layer. When the system failed to retrieve the correct chunk, the model filled in the gaps—producing responses that sounded plausible but were not grounded in approved sources.
Several fixes were attempted before engaging Verly AI: increasing model size to improve reasoning, expanding context windows to include more documents, manually rewriting help center articles, and adding rule-based guardrails on high-risk topics. None addressed the structural issue of retrieval precision at scale.
✅ Quick Summary
Against 1,200 real support tickets, the system achieved just 61% verified accuracy. With traffic increasing and compliance teams escalating concerns, the organization reached a decision point: the architecture had to change—not just the model.