We build chat and voice assistants grounded in your own documents and data using retrieval (RAG), so answers stay accurate and up to date. When a question needs a person, the conversation goes to your team with the full history attached.
Website chat
Answer product and pricing questions, capture leads and book calls around the clock.
Customer support
Resolve order-status, account and how-to questions; escalate everything else.
Voice agents
Pick up inbound calls, answer common questions and schedule callbacks.
Messaging channels
Run the same assistant on WhatsApp, Slack or your app, from one knowledge base.
What you get
- Knowledge base setup from your docs, FAQs and data
- Chat widget, voice line or messaging integration
- Human hand-off to your helpdesk or inbox
- Conversation logs and analytics
- Answer-quality test set and tuning
Common questions
Will the chatbot make things up?
We ground answers in your content and instruct the bot to say when it does not know. We also test it against real questions before launch.
Can it update itself when our docs change?
Yes. With retrieval, updating the source documents updates the answers. No retraining needed.
Can we try one on this site?
Yes. The chat and call buttons on this page are running our own assistant.
Further reading
All postsAI··1 min read
Before you build an AI agent, answer these five questions
Most agent projects that stall do so for boring reasons: unclear scope, messy data, nobody owning the edge cases. Here is the checklist we run first.
AI··1 min read
RAG or fine-tuning? A plain-English way to decide
Two ways to make a language model know about your business. They solve different problems, and picking the wrong one is expensive.
Tell us what you're building and we'll tell you how we'd approach it, whether that's AI, Web3 or just a really solid app.