AI-Powered Hiring Workflow

A confidential client needed to modernize their hiring pipeline. Manual candidate evaluation was slow, inconsistent, and creating bottlenecks for their recruiting team across multiple open roles.

I designed and built an end-to-end AI-assisted hiring workflow to automate candidate intake, evaluation, and routing — while keeping human judgment in the loop for critical decisions.

The system covered four core stages:

Candidate intake — structured data capture from multiple sources, normalized into a single pipeline

AI-assisted evaluation — LLM APIs scored and summarized candidates against role criteria, flagging confidence levels on each assessment

Human review checkpoints — ambiguous or low-confidence evaluations were routed to recruiters rather than auto-decided, preserving trust in the system

Recruiter visibility — a Supabase-backed dashboard gave the team real-time visibility into pipeline status, automation health, and review queues

Building this surfaced real operational tensions that shaped the final design:

Automation trust: Early versions over-automated. Recruiters didn't trust decisions they couldn't trace. I rebuilt the evaluation layer to surface reasoning alongside scores, not just outputs.

Uncertainty handling: The system needed to know what it didn't know. I added explicit confidence thresholds so low-certainty cases always escalated to human review.

Automation boundaries: Some decisions (final rejection, offer stage) were deliberately kept outside the automation scope. Knowing where to stop was as important as knowing what to automate.

Delivered a hiring workflow prototype that reduced manual routing effort in demo conditions. Validated with a structured demo across 50 recruiters, who tested the workflow end-to-end and provided direct feedback on the evaluation logic, review checkpoints, and dashboard visibility. Response was positive — recruiters found the human-in-the-loop model more trustworthy than fully automated alternatives they'd seen before. Iterative improvements are ongoing based on that feedback.

The human review checkpoint pattern and confidence-threshold escalation logic are now reusable components I carry into other automation projects.