AI & RAG
Auto-fill a security questionnaire spreadsheet in Airtable
Reads each unanswered row in an Airtable questionnaire table, retrieves the best-matching approved answer from your corpus.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled scan for unanswered rows
- ActionRead unanswered questionnaire rowsAirtable
- ActionRetrieve best-match approved answersOpenAI
- LogicRoute low-confidence rows to needs-review
- ActionAdapt answer to question wordingOpenAI
- OutputWrite proposed answer + confidence back to rowAirtable
What it does
Processes vendor security questionnaires that live as rows in Airtable. For every question still marked unanswered, it finds the closest approved answer in your corpus, adapts it to fit the question wording, and writes the proposed answer, the source reference, and a confidence score straight back into the row. Reviewers then flip rows from 'proposed' to 'approved'.
When to use it
Use this when questionnaires arrive as long grids and you track them in Airtable. It shines for high-volume, repetitive questionnaires where most answers already exist and you mainly need triage plus a first draft per row.
How it works
- 1A scheduled run scans the Airtable questionnaire table for rows with status 'unanswered'.
- 2Each question is embedded and matched against the approved-answer corpus.
- 3A branch routes low-confidence matches to a separate 'needs review' view instead of auto-drafting.
- 4An LLM rewrites the retrieved answer to match the question's exact phrasing.
- 5The proposed answer, source link, and confidence are written back into the row and its status set to 'proposed'.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect OpenAIModels, embeddings, files.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
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Run this workflow in your colony.
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