To build an AI customer support chatbot that actually helps, ground it in your own help content with RAG, connect it to your helpdesk, add a confident human-handoff, and evaluate its answers. That combination lets it deflect real tickets without inventing answers — the failure mode that erodes trust fastest.
The build
- Ingest your help docs, past tickets, and knowledge base into a vector index.
- Retrieve the most relevant chunks for each question (RAG), and re-rank for quality.
- Generate answers grounded strictly in retrieved content, with links to sources.
- Integrate your helpdesk (Zendesk, Intercom, etc.) so the bot has context and can log conversations.
- Add a clear human handoff when confidence is low or the user asks.
- Track deflection rate and answer accuracy with analytics and an evaluation set.
Where support bots fail
- Hallucination: answering without grounding, so it confidently makes things up.
- No handoff: trapping frustrated users instead of escalating to a human.
- Stale content: the knowledge base drifts and answers go wrong.
- No evaluation: quality silently degrades and nobody notices.
Our approach
We build support chatbots that are grounded, evaluated, and measured on real deflection — with the model kept swappable so you're never locked to one provider's pricing or quirks. It's the difference between a demo and a bot your customers actually trust.