A conversational agent that resolves 80% of tier-1 tickets before a human ever sees them. Retrieval-grounded, deflection-first, and CSAT-positive at 4.8.
Every answer is constrained to passages retrieved from Pinecone, with citations attached to each response. If retrieval confidence drops below threshold, the agent refuses to improvise and routes to a human instead.
This deflection-first design is what kept CSAT at 4.8 while automating 80% of tier-1 volume, users get correct answers or a fast handoff, never a confident fabrication.
Beyond FAQs, the agent calls real tools: password resets, order lookups, refund initiation and subscription changes, each gated by confidence and permission checks.
Sensitive or low-confidence cases escalate with the full transcript and proposed action pre-filled, so human agents resume in seconds rather than re-triaging from scratch.
| Component | Technology | Why |
|---|---|---|
| Reasoning & generation | OpenAI GPT-4o | Strong tool-calling and grounded, low-hallucination answers. |
| Orchestration | LangChain | Routing, retrieval chains and tool execution in one runtime. |
| Vector memory | Pinecone | Low-latency semantic search with per-tenant metadata filters. |
| Chat surface | Next.js | Streaming UI, embeddable widget, SSR for fast first token. |
| Backend services | Python | Embedding jobs, tool APIs and async escalation handlers. |
Responses are built only from cited Pinecone passages, suppressing hallucinations.
Tool calls fire only above a tuned confidence threshold, otherwise the agent escalates.
Low-confidence cases pass the full transcript and a proposed resolution to a live agent.
Per-namespace Pinecone filtering keeps each customer's knowledge and data separated.
A Next.js embeddable widget streams tokens for sub-second perceived response time.
CSAT and deflection signals drive weekly intent retraining and knowledge re-ranking.
“It answers when it's sure, acts when it's allowed, and steps aside the moment it isn't, that discipline is why automation didn't cost us CSAT.”
The AI Support Agent turned tier-1 support from a cost center into a deflection engine: 80% of routine tickets resolved automatically, resolution time down 60%, support cost down 35%, all while holding a 4.8 CSAT. By grounding every answer in retrieved, cited context and gating real-world actions behind confidence checks, it scales support volume without scaling headcount, and gets measurably sharper each week as live feedback refines its intents and knowledge base.