AI & RAG

Compliance Policy Answer Bot in Slack (Citation-Required)

Answers employee compliance questions in Slack from a frozen policy corpus, returning a grounded answer with source citations or an explicit refusal when no supporting passage…

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerchat
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEmployee runs /policy slash command in SlackSlack
  • ActionEmbed question and retrieve top passages from pgvector corpusSupabaseSupabase
  • LogicCheck retrieval similarity against grounding threshold
  • ActionGenerate citation-constrained answer with OpenAIOpenAI
  • OutputReply in Slack thread with cited answer or refusalSlack

What it does

Lets any employee ask a compliance question via a Slack slash command and get an answer grounded strictly in your approved policy documents. Every answer carries citations back to the exact policy pages. If no passage supports an answer, the bot refuses rather than guessing.

When to use it

Use it when staff repeatedly ask the compliance team "is X allowed?" and you need consistent, auditable answers that never invent rules. Ideal for SOC 2, HIPAA, or internal code-of-conduct corpora that are frozen between review cycles.

How it works

  1. 1An employee triggers the bot with a slash command in Slack.
  2. 2The question is embedded and matched against the indexed policy corpus in Supabase pgvector to retrieve top passages.
  3. 3A logic gate checks the best similarity score against a threshold; weak matches route to the refusal path.
  4. 4OpenAI generates an answer constrained to the retrieved passages, with inline citation markers.
  5. 5The bot replies in-thread with either the cited answer or "No policy covers this — please ask Compliance directly."

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect SupabaseTables, auth, storage, edge functions.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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