KnowledgeBase

A generic model answers generically. A good AI agent needs vertical-specific knowledge: the objections real customers raise in that industry, the tactics that actually close, the vocabulary professionals use. KnowledgeBase is that library.

The structure is consistent across business types — dentistry, auto-service, legal services, and dozens more. Each type opens with an insider overview packed with the non-obvious knowledge that separates an expert from a novice. Below the overview sit sub-topics: concrete conversation directions, objection patterns, advanced sales techniques.

Every sub-topic is backed by deep content — tactics, phrasings, worked examples. All of it in English, which is the working language of the base; translation happens at agent-response time when needed.

Content is produced by a pipeline, not hand-typed. The pipeline performs web research using a top-tier model and current sources, structures the findings, and commits them to the base. The result is not fluff — it is a dense set of facts, frames, and techniques an agent can actually use.

Admins kick off ingest jobs per business type and watch live progress: a timer, a journal of the steps, and per-stage logs. Re-running ingest for a type replaces the old content entirely — the base stays current rather than bloating with stale versions.

Every element has a status: draft, published, deprecated. Only published content reaches agents, which keeps half-baked research from leaking into customer conversations.

Agents consume the base automatically. When a customer asks a question, the agent identifies the relevant knowledge and incorporates it into the response — no manual lookup required. Business types map to the categories the site crawler recognises, so a customer's industry is inferred end-to-end.

The bottom line: a chatbot that sounds generic becomes an agent that sounds knowledgeable. That shift is what turns a conversation into a closed deal.