Why support hallucinations are a business problem, not a model quirk
A hallucinated refund policy is a promise your company now has to argue about. Courts and regulators have already treated chatbot statements as binding company representations — famously in Air Canada's tribunal loss over its chatbot's invented bereavement-fare policy. The bar for a customer-facing bot is therefore higher than "usually right": every answer needs provenance, and every gap needs an honest exit.
The three mechanisms that make answers trustworthy
- Grounding: the model answers only from retrieved passages of your content (see what is a RAG chatbot). Flocci Chat retrieves with hybrid vector + full-text search and instructs the model to answer strictly from that context.
- Citations in the reply: sources stream alongside the answer as chips a visitor can open. This isn't decoration — visible provenance disciplines the whole pipeline, because a wrong citation is immediately checkable.
- A confidence gate with real exits: Flocci Chat scores retrieval confidence every turn. Weak retrieval routes to a clarifying question ("are you asking about credit cards or bank transfers?") or a human handoff that opens a real support ticket — with the transcript attached — in the built-in inbox. Sensitive topics escalate automatically.
Questions to ask any vendor claiming "no hallucinations"
- Show me an answer's sources — in the widget, as the visitor sees it, not in an admin log.
- What exactly happens when retrieval finds nothing? (The honest answers are "clarify" or "handoff". "The model does its best" means it guesses.)
- Am I charged for turns the bot couldn't answer? (Flocci Chat: no — unanswered and clarifying turns are free; only answered messages cost a credit.)
- Can I see what visitors asked that my content couldn't answer? (Flocci Chat's unanswered-intents analytics turn every gap into a content fix.)
Trust is also an operations discipline
Grounding gets you correct answers; operations keep them correct. Flocci Chat's dashboard tracks answer quality evals, reliability/SLO metrics, top questions and knowledge gaps, with a launch checklist gating go-live. The result is a support surface you can put in front of customers and defend afterwards — every answer traceable to a page you actually published.