Recruiting
AI candidate evaluation engine
A hiring intelligence platform
Recruiters stopped reading every candidate cold, and started from a ranked shortlist with the reasoning already laid out.
The situation
Hiring managers and recruiters were manually reading through dozens of candidate records per role: resumes, ATS entries, screening notes, to decide who was worth an interview. Evaluation was inconsistent from one reviewer to the next, slow, and easy to get wrong when the stakes are high. Their stack already worked: an ATS, assessment tools, scheduling, and internal notes. The problem was not the tools. It was the manual work of moving data between them, re-reading the same material, and applying criteria differently every time.
What we built
A multi-agent evaluation layer that sits on top of what they already use. We did not rebuild their ATS or ask them to migrate. We connect the systems they rely on, pull candidate data in automatically (resume, work history, screening answers, assessment results), and keep everything in sync so recruiters stop being the glue. The pipeline extracts structured signal from unstructured text: experience depth, role fit, career patterns, skills and certifications, reasons for leaving, employment gaps, and anything else the role demands. Each candidate is scored and ranked against the requirements defined for that opening (must-haves, nice-to-haves, and automatic disqualifiers), with gaps flagged explicitly and in context instead of buried in a wall of resume text. Reviewers get a ranked shortlist, per-candidate reasoning, side-by-side comparisons, and audit trails. Human checkpoints stay at every decision point before anyone moves forward.
What changed
Recruiters move from reading every record cold to reviewing a ranked shortlist with the reasoning already visible. Evaluation stays consistent across reviewers. The team keeps their existing tools and workflows; the new layer removes the repetitive reading, copying, and re-scoring that used to sit on top of them.
Tell us where the hours go.
