AI AGENTS

Extract RFP requirements into a capability gap matrix

Reads a new RFP from Dropbox, pulls out every discrete requirement with an LLM, scores each against your capability library, and writes a color-coded gap matrix to Notion.

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew RFP file added to Dropbox inbound folderDropboxDropbox
  • ActionDownload document text from DropboxDropboxDropbox
  • ActionExtract atomic requirements with OpenAIOpenAI
  • LogicScore each requirement Meets / Partial / Gap
  • ActionWrite gap matrix to Notion databaseNotionNotion
  • OutputPost gap summary and link to SlackSlack

What it does

When a procurement RFP lands in a watched Dropbox folder, this workflow parses the document, extracts each individual requirement as a structured line item (section, mandatory vs. optional, evaluation weight), then compares every requirement against your stored capability statements. It produces a gap matrix marking each item as Meets, Partial, or Gap, and publishes it as a Notion database the bid team can sort and filter.

When to use it

Use this when your team responds to formal RFPs and the first painful hour is always hand-copying requirements into a spreadsheet. It turns a 60-page PDF into a triaged matrix before anyone reads page one.

How it works

  1. 1A new file in the Dropbox `/rfps/inbound` folder triggers the run.
  2. 2The document text is downloaded and sent to OpenAI, which returns a structured list of atomic requirements.
  3. 3Each requirement is matched against your capability library and scored Meets / Partial / Gap.
  4. 4The scored rows are written to a new Notion database with conditional formatting.
  5. 5A Slack message posts the gap count and a link to 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 NotionPages, databases, comments.
  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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