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…
How it runs
The automated pipeline, trigger to output.
- TriggerNew vendor row added in AirtableAirtable
- ActionRead RFP requirements checklist from AirtableAirtable
- 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 AirtableAirtable
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
- 1A new vendor row is created in the Airtable requirements base.
- 2The agent reads the full requirements checklist for the RFP.
- 3For each requirement it runs targeted Exa research scoped to the vendor.
- 4An OpenAI pass returns a pass, partial, or fail verdict plus confidence per requirement.
- 5A branch routes any low-confidence verdicts to a human review flag.
- 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.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect ExaNeural search across the web.
- 3Connect OpenAIModels, embeddings, files.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.

Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.
