A ChatGPT-powered support bot that cut response times and autonomously handled a large share of incoming inquiries, freeing staff for complex, high-value cases.
The support queue grew faster than the team could hire. Customers waited, agents burned out on repetitive questions, and the same answers were typed a hundred times a day.
The challenge
A support bot has to actually resolve the routine cases — not deflect them into a dead end — while knowing when to hand a hard problem to a human without losing context.
Our approach
We deployed an LLM support assistant grounded in the client's help content, tuned to resolve common inquiries end-to-end and to escalate cleanly — with full conversation context — the moment a case needs a person. It handles the repetitive volume so agents keep the hard, high-value work.
Under the hood
- Model — ChatGPT / LLM grounded in help content
- Automation — End-to-end resolution of common inquiries
- Handoff — Clean escalation with full context
- Availability — Round-the-clock coverage
What we built
- An LLM assistant grounded in the client's knowledge base
- End-to-end resolution for routine inquiries
- Context-preserving handoff to human agents
- Always-on first response across channels
Results
- Response times down 40%
- Half of all inquiries resolved without an agent
A Lightning Leap case study — one of the AI systems we've designed, built, and shipped.