BotIQ: How RAG Powers Zero-Hallucination Support

The fear that stops most teams from deploying an AI agent is simple: what if it makes something up? A support bot that confidently invents a refund policy is worse than no bot at all. This is exactly the problem BotIQ is built to solve, using an approach called Retrieval-Augmented Generation (RAG).

Why generic chatbots hallucinate

A large language model on its own answers from its training data — it doesn't know your prices, your policies, or your product. Ask it a specific question and it will often produce a plausible-sounding but wrong answer. That's a hallucination, and in support it's a liability.

How RAG changes the game

RAG grounds the model in your content. Instead of answering from memory, BotIQ:

• Learns your business by ingesting your website, help center and docs.
• Retrieves the most relevant passages for each customer question.
• Generates an answer strictly from those passages — and cites the source.

If the answer isn't in your knowledge, BotIQ says so and hands off to a human rather than guessing. That's what "zero-hallucination" means in practice: answers are traceable to a source you control.

Why source citations matter

Every BotIQ answer links back to where it came from. Customers trust it more, and your team can audit it instantly. When a policy changes, you update the knowledge base once and every future answer reflects it — no retraining required.

Always improving, always in your control

Because the intelligence lives in your knowledge base, improving BotIQ is as simple as improving your help content. Add an article, and the AI can answer that question the next second — across every channel.

See it on your own content

The best way to judge accuracy is to point BotIQ at your real help center and ask it hard questions. Every new workspace gets a free trial to do exactly that.

Train BotIQ on your knowledge →