AI AGENTS

Split inbound RFP PDF into a draft compliance matrix

When a new RFP PDF lands in a Dropbox folder, an agent extracts every numbered requirement into discrete line-items and assembles a draft compliance matrix in Airtable.

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew PDF added to Dropbox RFP folderDropboxDropbox
  • ActionDownload PDF contentsDropboxDropbox
  • ActionExtract requirement line-items with OpenAIOpenAI
  • ActionCreate one Airtable row per requirementAirtableAirtable
  • OutputNotify bid team in Slack with matrix linkSlack

What it does

Watches a Dropbox "Inbound RFPs" folder, and the moment a new PDF appears it reads the whole document, isolates each individual requirement (the "shall", "must", and numbered clauses), and writes one Airtable row per requirement. Each row is pre-seeded with the requirement text, its section reference, and empty Response / Owner / Status columns — a ready-to-fill compliance matrix.

When to use it

Use it when your team receives long RFP or RFI documents and someone currently spends hours manually transcribing requirements into a tracking sheet before the real work starts. It collapses that intake step to seconds and guarantees no clause is missed.

How it works

  1. 1A new PDF is detected in the watched Dropbox folder.
  2. 2The file content is pulled down for processing.
  3. 3OpenAI parses the document and returns a structured list of requirement line-items, each with a section reference and clause text.
  4. 4Each line-item is written as a row in the Airtable compliance-matrix table.
  5. 5A Slack message tells the bid team how many requirements were captured and links the matrix.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DropboxFiles and folders.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect AirtableBases, tables, views, automations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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