AI & RAG

Nightly audit of approved RFP answers for policy drift

Each night, re-checks every approved questionnaire answer against the current version of its cited Confluence policy and opens a Linear issue for any answer whose source has…

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionLoad approved answers with citationsAirtableAirtable
  • ActionFetch current policy page versionsConfluenceConfluence
  • LogicKeep only materially drifted sources
  • OutputOpen Linear issue per drifted answerLinearLinear

What it does

Keeps your approved security answers honest over time. Policies change; answers that quoted last quarter's encryption standard quietly go stale. This compares each stored citation's version against the live Confluence page and surfaces every answer that now contradicts current policy.

When to use it

Run it continuously once you maintain a library of approved answers. Essential for regulated buyers where shipping an outdated SOC 2 or data-handling claim is a real liability.

How it works

  1. 1A nightly schedule kicks off the audit.
  2. 2Every Airtable row marked "Approved" with a citation is loaded.
  3. 3For each, the current Confluence page version is fetched and compared to the version recorded at approval time.
  4. 4A logic step keeps only rows where the source version advanced and the text materially changed.
  5. 5OpenAI explains what shifted and whether the old answer is now wrong.
  6. 6A Linear issue is opened per drifted answer, tagged for review with a link to both versions.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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