Intelligent Candidate Scoring
Context-aware ranking that surfaces the most relevant candidates for each role.
Use the simplest reliable path for each resume, and spend model capacity where it matters.
Context-aware ranking that surfaces the most relevant candidates for each role.
The brief
Designed around the real friction.
High-quality parsing can become expensive when every document is sent through the most capable model. The research problem is to lower the average cost without degrading the fields recruiters actually depend on.
The working direction is a hybrid router: deterministic rules for obvious structure, smaller task-specific models for common patterns and human review for low-confidence fields. Evaluation is field-level so cost and quality can be tuned together rather than argued about in the abstract.
Optimization
Cost per processed resume
Quality bar
Field-level accuracy
Safety valve
Confidence-based review
How it works
A system with a clear next move.
Classify document complexity before choosing an extraction path.
Use lightweight rules and models for the majority of predictable fields.
Escalate only the ambiguous cases, then measure quality against cost.
Project notes
What we are building toward
A measurable path toward lower parsing cost, stronger traceability and a more responsible use of AI in a workflow that affects real candidates.
Overview
An AI layer on top of the ATS that scores and ranks applicants against job requirements using retrieval-augmented evaluation.
Product focus
Fast shortlisting, explainable scores, and bias-aware evaluation signals that hiring teams can trust.