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AI & HR Technology
This is a concept case study: an illustrative engagement that shows how our team would approach this kind of problem. It isn't a completed client deployment, the interface shown is a design mockup, and no business results are claimed.
An AI-assisted recruitment platform that helps hiring teams review applications faster. It parses resumes, matches candidates to role requirements and organizes the pipeline, while final decisions always stay with people.
High-volume roles attract hundreds of applications. Recruiters skim resumes inconsistently, strong candidates get missed, and keyword filters reject people whose experience is relevant but described differently.
Language models extract skills and experience from resumes into a structured profile, and a matching layer explains how each candidate aligns with the role's criteria. Recruiters see the reasoning, can override any assessment, and work from a pipeline board with automated, configurable screening steps.
Structured extraction of skills, experience and qualifications from any format.
Explainable match summaries against each role's must-have and nice-to-have criteria.
Configurable screening questions and scheduling, always reviewed by a recruiter.
Pipeline stages, time-in-stage and source reporting per role.
Protected attributes excluded from matching, with regular bias reviews.
Shared scorecards, comments and interview feedback in one place.
How the system is layered, from the interface people use down to the checks that guard every release.
These are design goals for the solution, not measured results.
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We'd be glad to walk through how we'd approach your version of this problem: the architecture, the risks and a realistic plan.