Chat

Chat is the surface most users live in: ask a question in plain language, get a streamed answer grounded in your organisation’s extracted knowledge, with citations on every claim. It’s the difference between “search returned 12 rules” and “here’s how deal approval works here, and here’s the evidence.” Chat is not a vector-search wrapper around a language model. Answers are assembled from the full hybrid query pipeline over knowledge that was extracted, structured, versioned, and human-reviewed before the conversation started. The model writes the prose; the knowledge layer supplies the facts.

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How an answer is produced

1. The message runs through the full query pipeline: the same signals, fusion, and reranking as the API.
2. The model generates an answer bounded by the retrieved knowledge, not by its general training. Seyn’s value is your organisation’s knowledge, not the model’s opinions.
3. The response streams token by token, with citations linking each claim to the rules it came from. From a citation, the provenance chain continues down to raw source records.
4. The conversation persists as a thread; history is browsable and resumable.

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Grounding and honesty

• Citations are not decoration. Every substantive claim carries them, and a citation resolves to a real rule, which resolves to real evidence. An answer you can’t drill into is an answer you shouldn’t trust.
• The query bounds the generation. If the knowledge library doesn’t contain the answer, the assistant says so rather than improvising. The failure mode is “I don’t have that,” not confident fiction.
• Review status flows through. Answers built on unreviewed (inferred) rules are still answers, but the citations show their status, so a careful reader can weigh them.

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Operational details

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Common mistakes

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