You can’t launch AI support on knowledge you haven’t audited.

Hallucinations usually happen when the model can’t find a clear answer in the source material it retrieves from. Meihaku audits your support docs, tickets, macros, and notes for the gaps and conflicts that force the model to guess, then gates which intents your agent is cleared to answer. Many teams pair Meihaku as the pre-flight check with runtime tools like Cleanlab or Ada-style monitoring as the in-flight safety net.

Three common patterns: (1) gaps — the customer asked something not covered in any source, so the model fills in plausible-sounding nonsense; (2) conflicts — one support source says one thing and another says something else, and the model picks the wrong one; (3) stale or out-of-date content. Meihaku surfaces all three before launch so you can fix them rather than ship them.

A two-layer approach. Pre-flight audits (Meihaku) catch the gaps and conflicts in your sources before the AI agent ever has to retrieve from them. Runtime monitoring (Cleanlab, Ada, others) catches the residual hallucinations that slip through. The fastest single improvement is usually source cleanup — no amount of runtime checking can save you from a knowledge base the model can’t retrieve clearly from.

Yes — and it’s the cleanest case to start with. Meihaku audits your support knowledge before you commit to a vendor. You’ll know in advance which intents are well-supported, which need source cleanup, and which can’t be answered safely by any agent. Most teams find the audit changes which agent they pick, because some handle gap-prone intents better than others.

Meihaku is the readiness layer that runs before and alongside AI support agents, not a replacement. Tools like Intercom Fin, Decagon, Sierra, and Maven generate answers at runtime. Meihaku audits whether your knowledge base actually supports those answers in the first place, surfacing conflicts, gaps, and ungrounded intents before they reach a customer. Most teams use Meihaku to pre-flight an AI agent rollout, then keep it running as a governance layer.

Cleanlab and Ada-style runtime tools score AI responses as they’re produced and can block bad outputs in real time. Meihaku works one layer earlier: it audits the source knowledge your AI agent retrieves from, so the bad answer never gets generated in the first place. The two are complementary — Meihaku as the pre-flight check, runtime tools as the in-flight safety net.

Google Drive folders containing your support policies, SOPs, macros, and FAQ docs, plus Zendesk source bundles. Intercom, Notion, Confluence, and broader helpdesk connectors are on the near-term roadmap. Meihaku connects read-only and never writes back to the source — what you see in the audit is exactly what your team approves.

No. Meihaku is complementary. Your AI agent (Fin, Decagon, Sierra, custom) still answers customers. Meihaku decides which intents are cleared for that agent to handle, with cited evidence, and exports the approved answer set. Unsupported or conflicting intents stay out of automation until your team has source evidence or a handoff path.

Yes — that’s the core workflow. Meihaku drafts cited answers from your sources, your team reviews each one with the source line attached, and only approved intents become the boundary your AI agent operates inside. Nothing reaches production until you greenlight it.

When the same intent (for example, the refund window for international orders) resolves to different answers across support sources, Meihaku flags it as a conflict and shows both source lines side-by-side. Your team picks the canonical answer, which becomes the approved version exported to your AI agent.

That’s the most common starting point — and exactly what Meihaku is designed for. You don’t need to rewrite anything before connecting. Meihaku reads what you have, drafts cited answers from the strongest evidence, flags gaps and conflicts, and lets your team triage them in priority order. The first audit usually surfaces the small set of intents blocking most of the customer-risk conversations.

Connecting a read-only source takes minutes when credentials are ready. The first useful readiness map depends on source volume, but the workflow is designed so teams can start with one high-risk folder or ticket bundle before expanding the audit.

No. Customer data is processed in your isolated workspace. We don’t train foundation models on your content, and we don’t share data across customers. Source content is processed transiently and not retained beyond what’s needed for citations.