Accuracy in an AI agent usually comes down to one thing: did it find the right passage in your content before answering? Most tools take a single pass - one search, one answer, right or wrong. Wonderchat uses multi-pass retrieval, so when the first search comes back weak, it reformulates and tries again rather than answering from a bad match.
That extra step is the difference between an answer you can publish and one you have to apologise for. The agent works to actually find the relevant passage, grounds its answer in it, and cites the source - and when it genuinely can't find support, it says so instead of forcing an answer. Speed doesn't suffer: the retrieval happens fast enough that customers get a quick response, just a more accurate one.
Underneath, it's layered with the rest of the accuracy system - grounding, citation, confidence scoring, and the correction loop - so accuracy isn't one trick but several reinforcing each other. That's what "enterprise-grade" actually means here: not a marketing tier, but a stack of safeguards that make the agent safe to deploy at scale.
Across all customers, Wonderchat resolves about 76% of conversations on its own, at a speed customers don't wait on. It's part of a SOC 2 Type II certified, GDPR-compliant platform. Accuracy you can stand behind, fast enough to use.
FAQ
How does Wonderchat keep its answers accurate?
It uses multi-pass retrieval - reformulating a weak search before answering - layered with grounding, citation, confidence scoring and a correction loop, so answers are supported rather than guessed.
Does higher accuracy make it slower?
How much can it resolve accurately on its own?
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