AI & RAG

Grounded Audit Answer-Bot with Per-Clause Source Citations

Answers auditor and reviewer questions against a frozen compliance evidence corpus and returns every claim with an inline citation back to the exact source document and clause.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerchat
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerAuditor question received via chat
  • ActionEmbed question and retrieve top clause chunks from pgvectorPostgreSQLPostgres
  • LogicGate on top similarity score; reply 'no evidence' if below threshold
  • ActionGenerate clause-grounded answer with per-sentence citationsOpenAI
  • OutputReturn answer with clause-to-source citation list

What it does

Gives auditors, security reviewers, and compliance staff a chat endpoint that answers questions strictly from your frozen evidence corpus. Every sentence in the answer carries a citation pointing to the source document and the specific clause it came from, so a reviewer can trace any claim. If retrieval finds no supporting passage above the similarity threshold, the bot says it cannot answer rather than guessing.

When to use it

During SOC 2, ISO 27001, or HITRUST audit windows when reviewers fire dozens of evidence questions and you need defensible, traceable answers instead of hand-searched policy PDFs.

How it works

  1. 1An auditor question arrives over the chat webhook.
  2. 2The question is embedded and matched against pre-indexed clause chunks in pgvector.
  3. 3A relevance gate checks the top match score; below threshold it short-circuits to a 'no evidence found' reply.
  4. 4OpenAI composes an answer constrained to the retrieved clauses, emitting per-sentence citation markers.
  5. 5The grounded answer plus a clause-to-source citation list is returned to the caller.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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