AI customer support that never makes things up
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LYQN is a self learning AI support agent that grounds every answer in your own website, documents, and FAQs, so it never invents information about your business. It connects web chat and WhatsApp into one inbox, and hands off to a human teammate the moment it’s unsure, getting sharper after every conversation automatically. Goes live with a single line of code, no technical knowledge needed. Start your free trial today.
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WebCurate.co
Sounds like a solid direction, especially grounding answers in your own docs. That’s usually the biggest problem with support bots, they start guessing when they don’t know stuff.
I’m a bit curious how strict the "never makes things up" part is in real use though. Like if the docs are incomplete or outdated, does it just refuse, or still try to form an answer? That part usually decides if users trust it or not.
@hosseinyazdi Good distinction to draw out, because those are actually two different failure modes for us.
If the docs are incomplete, meaning there’s genuinely no relevant content for that question, the retrieval step comes back with low similarity, and that’s what triggers escalation to a human instead of forcing an answer. That part we’re fairly confident in.
Outdated docs are the harder case honestly. If the content is technically in the knowledge base but wrong, like an old shipping policy that changed last month, the AI has no way to know that, it’ll answer from what it’s been given and sound just as confident as if it were correct. That’s less an AI problem and more a ‘keep your source content fresh’ problem, so part of what we’re working on is nudging businesses to update their content rather than pretending the model can detect staleness on its own.
Never makes things up is a strong promise but in practice, I’m curious how strict the grounding actually is in edge cases.
@sadam_ansari2 Good question, and the honest answer is it’s a strong default, not an absolute guarantee. The moment a question doesn’t closely match what’s in the knowledge base, or the model’s confidence is low, it escalates straight to a human instead of guessing, so the customer still gets a fast, accurate answer instead of a wrong one. The part I like most is what happens after, that human response gets fed back into the system, so the next time a similar question comes up, the AI actually knows how to handle it. So edge cases don’t just get caught, they make the whole thing better over time.
If you run a small business and decide to give LYQN a real try this week, genuinely, thank you. Early users like you are the ones who shape what this becomes, every bit of feedback gets read and acted on, not just collected.