AI AGENTS

RFP Vendor Fit Scoring from a Requirements Sheet

When a requirements row is added to Airtable, an agent researches the named vendor against each requirement, scores compliance, and writes a pass/fail/partial verdict…

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew vendor row added in AirtableAirtableAirtable
  • ActionRead RFP requirements checklist from AirtableAirtableAirtable
  • ActionResearch vendor per requirement with ExaExa
  • ActionScore pass/partial/fail with OpenAIOpenAI
  • LogicFlag low-confidence verdicts for human review
  • OutputWrite verdicts and fit percentage to AirtableAirtableAirtable

What it does

Grades vendors against a structured RFP requirements sheet. Each time you add a vendor to the requirements table, the agent researches whether that vendor meets each line item, assigns a pass, partial, or fail with a confidence score, and links the source backing every verdict. Your RFP matrix fills itself in.

When to use it

Ideal for formal RFP or RFI processes where requirements live in a spreadsheet and you need consistent, auditable scoring across many vendors. It removes the human inconsistency of different reviewers grading the same requirement differently.

How it works

  1. 1A new vendor row is created in the Airtable requirements base.
  2. 2The agent reads the full requirements checklist for the RFP.
  3. 3For each requirement it runs targeted Exa research scoped to the vendor.
  4. 4An OpenAI pass returns a pass, partial, or fail verdict plus confidence per requirement.
  5. 5A branch routes any low-confidence verdicts to a human review flag.
  6. 6Scored verdicts, evidence links, and a fit percentage are written back to the Airtable row.

Set it up

What you configure once, before turning it on.

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

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