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I built a working AI recruitment screening demo that shows how a hiring team can use AI without handing over the final decision.
The flow is simple:
A job spec goes in.
Candidate profiles are screened against a weighted guideline.
Each candidate gets a score, band, evidence quotes, and risk flags.
Then everything stops at a human review queue.
No auto-rejects. No silent ATS updates. No “AI decided this person is out”.
The system includes:
Candidate pipeline table
Evidence-based screening view
Human review queue
Approve / reject / snooze decisions
ATS deployment plan
Audit log for compliance
n8n workflow export with a human-in-the-loop Wait node
Optional live Claude screening path
The important part is the architecture: deterministic logic owns the workflow, gating, state and audit trail. The AI helps with judgement and screening, but the human owns the final decision.
Built with Next.js, TypeScript, Claude API, n8n workflow architecture, and a gated ATS sync pattern.
This is the kind of system I like building: AI that makes the work faster, but still respects the places where people need control.
Shorter Version
I built an AI recruitment screening pipeline with a human approval gate.
It screens candidates against a rubric, shows evidence quotes and risk flags, then routes every candidate into a review queue before anything reaches the ATS.
No auto-rejects. No silent updates. Human-in-the-loop by design.
Built with Next.js, TypeScript, Claude API and n8n workflow architecture.
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I'd use the human approval step to capture corrections, not just a yes/no decision. If a reviewer changes the extracted lead details, does the workflow update Airtable and the reporting record before sending the email?