Conversational agent that resolves 78% of L1 tickets autonomously across email, chat and voice — with full handoff context for human reps.
We started with a five-week discovery: shadowing 12 agents across three shifts, clustering 6 months of historical tickets, and instrumenting every channel to baseline true handle time. The discovery surfaced a hard truth — 70% of inbound volume mapped to just 38 intents, but tribal knowledge was trapped in Slack and 12k knowledge-base articles of inconsistent quality.
The agent is a LangGraph state machine that triages intent, retrieves grounded context from a Pinecone-backed RAG layer, drafts a response, and runs a deterministic guardrail pass before sending. Tools include refund execution, order lookup, subscription pause and a sandboxed shell for safe knowledge edits. A confidence threshold drives the handoff: anything under 0.82 routes to a human with the full transcript, retrieved snippets and the agent's proposed action attached.
Three weeks after go-live the team retired their tier-1 outsourcer. We continue to ship weekly: a hallucination detector trained on flagged conversations, a feedback loop from CSAT into prompt tuning, and a voice channel that hit production in month four.
The client's 40-agent support team was buried under 18,000 monthly tickets — 70% repetitive. SLA breaches were rising and CSAT was sliding below 80%.
We built a multi-channel AI agent grounded in their knowledge base via RAG, wired into Zendesk, email and voice. The agent triages, drafts, executes refunds and escalates with a full transcript when it isn't confident.
Designed for clarity. Built to scale.
Retrieval-augmented answers from 12k KB articles
Live human handoff with full conversation context
Voice agent with sub-second turn latency
Built-in QA scoring & hallucination guardrails
