Ask in the group chat; get an answer with the source linked. Retrieval is scoped to what the asker may see — and what she cannot find, she says she cannot find. Answers must cite a verbatim source passage or they are downgraded.
Hedy's knowledge base assistant answers from your company's documents with every claim cited line-by-line to its source. Retrieval is filtered server-side by the asker's ACL, so people only see what they're cleared to see. When the evidence gate can't find a supporting passage, Hedy says it doesn't know instead of guessing. It runs self-hosted on your infrastructure, keeps knowledge private, and replaces a whole answer-desk team with one accountable AI employee.
Last updated: 20 July 2026
Git repositories, Notion, Confluence and meeting notes — synced and chunked with citations preserved.
Questions in Feishu, Lark or Slack. Hybrid retrieval finds the passage; the answer cites it.
Server-side filtering by the asker’s tags. Documents above a person’s clearance are not summarized, hinted at, or counted into answers.
No hit means "not found, here is the closest document and the gap has been logged" — never an invented answer.
Most cloud AI assistants retrieve first and check permissions never. Hedy inverts that. Every query runs against a per-asker ACL enforced on the server, before retrieval, not in the prompt. If a passage sits in a space the person can't read, it never enters the context window. The same question from a sales rep and a finance lead returns different answers because they're cleared for different sources. Access is a property of the retrieval layer, not a suggestion to the model.
The evidence gate is the core of the design. Hedy's answers must quote the source passages that support them, verbatim, with a citation to the document and location. If the gate finds no passage that grounds a claim, that claim doesn't ship. When nothing in the corpus answers the question, Hedy says so plainly rather than filling the gap with a plausible-sounding hallucination. "I don't know, here's what I searched" is a valid, expected output, not a failure.
This is one AI employee doing the work of an answer desk, not a chat toy bolted onto Slack. Hedy is a first-class citizen in Feishu/Lark and Slack, runs on your own model keys including local vLLM or Ollama, and deploys via Docker compose in about 30 minutes. It's fully self-hosted with no sub-processors, so your knowledge base and every question asked against it stay inside your infrastructure.
Governance is the product, not a settings page. Every answer and its citations land in an append-only audit log you can replay. Retrieval respects ACLs by construction. Red-line behavior tests check that Hedy refuses out-of-scope or unauthorized questions, and licensing is offline via Ed25519, so an air-gapped deployment needs no phone-home. You get a knowledge assistant you can actually put in front of an auditor.