HR & RECRUITING
Score and rank inbound resumes into an Airtable shortlist
When a new application lands in Airtable, an AI scorer rates the resume against the role's must-haves and updates the record with a 0-100 fit score, rationale…
How it runs
The automated pipeline, trigger to output.
- TriggerNew applicant row added in AirtableAirtable
- ActionAssemble resume text and role rubric into prompt
- ActionScore fit 0-100 with rationale via OpenAIOpenAI
- LogicMap score to Strong / Maybe / Reject tier
- OutputUpdate Airtable record with score, rationale, and tierAirtable
What it does
Turns a raw application queue into a ranked shortlist. Each new candidate row is scored against the job's required and preferred criteria, then written back to Airtable with a numeric fit score, a short rationale, and a tier label.
When to use it
Use it when applications arrive faster than a recruiter can read them and you want consistent, criteria-based first-pass screening rather than gut-feel triage. Best for high-volume roles where the must-have list is well defined.
How it works
- 1A new row in the Airtable Applicants table triggers the flow.
- 2The candidate's resume text and the role's scoring rubric are assembled into a prompt.
- 3OpenAI scores fit 0-100 and returns a one-paragraph rationale plus matched and missing requirements.
- 4A logic step maps the score to a tier: 80+ Strong, 50-79 Maybe, below 50 Reject.
- 5The Airtable record is updated with the score, rationale, and tier, and the row's status moves to Screened.
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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