AI-Assisted Candidate Matching for Recruiters
Candidate matching on TAL.co uses structured job requirements and multi-dimensional candidate signals to produce a ranked shortlist — with recruiter-reviewed reasoning at every step.
AI-generated ranking signals. Recruiter-reviewed — never an automated reject.
Matching built on structure, not keyword overlap
Keyword-based matching produces candidates who resemble job descriptions, not candidates who can do the job. TAL.co's fit scoring is built on structured candidate data — skills-graph depth, career trajectory, role-type history, and performance signals — evaluated against structured job requirements captured during intake.
Every fit score is accompanied by a transparent reasoning summary: which dimensions scored high, which scored lower, and why a candidate is worth reviewing despite any gaps. Recruiters review these summaries before any submission — the system is decision support, not a decision-maker.
How candidate matching works
Multi-dimensional fit scoring
Candidates are scored across skills match, experience depth, career trajectory, culture-context indicators, and compensation alignment — each dimension weighted by the role's requirements.
Transparent reasoning
Every score includes a plain-language summary explaining why a candidate ranks where they rank. Recruiters can override scores with notes that improve future calibration.
Candidate rediscovery
The matching engine surfaces candidates from your existing graph who were not originally sourced for a search but fit its requirements — turning your pipeline history into a compounding asset.
Hiring-manager feedback integration
Every submission rating from a hiring manager flows back into the matching model, calibrating future scores to that client's actual preferences over time.
Fit score breakdown
Transparent, multi-dimensional scoring with plain-language reasoning for every ranked candidate.
AI-generated ranking signals. Recruiter-reviewed — never an automated reject.
Matching performance metrics
Human in the loop — always
TAL.co's matching system is AI-assisted and recruiter-reviewed. The platform provides ranking signals and reasoning as decision support. Recruiters decide which candidates to submit. Hiring managers decide who to interview. TAL.co never decides who gets hired.
Questions, answered
Can I adjust the weighting of matching dimensions for a specific search?
Yes. You can adjust dimension weights per search based on hiring manager input or your own judgment. The system records these adjustments and can apply them to future similar searches.
How does the system handle candidates who lack structured data?
Candidates with sparse profiles receive a lower confidence indicator alongside their score. The system flags what data would improve score accuracy — and suggests asking the candidate to complete specific fields.
Does matching work across all role types?
Yes, with role-type-specific calibration. Technical, executive, sales, legal, and generalist roles use distinct scoring models. The intake brief determines which model applies.
Related
Submit candidates you believe in
AI-assisted fit scoring with transparent reasoning — decision support for recruiters, not a replacement for judgment.