Ertas for Resume Screening & HR

Fine-tune models that evaluate resumes against your specific role requirements, skills taxonomy, and hiring criteria — consistently and at scale.

The Challenge

Talent acquisition teams at growing companies review hundreds or thousands of applications per open role. Manual resume screening is time-consuming, inconsistent, and prone to unconscious bias. Different recruiters apply different standards to the same role, qualified candidates are overlooked during high-volume periods, and the pressure to move quickly leads to superficial reviews that miss transferable skills or non-traditional backgrounds.

Existing AI screening tools use keyword matching that is easily gamed and fails to evaluate actual qualification. A candidate who lists every technology keyword in their resume scores higher than a genuinely skilled engineer with a concise resume. These tools also cannot adapt to organization-specific requirements — your definition of 'senior' may differ from the generic industry standard, and the specific technology stack, domain experience, and soft skills that predict success at your company are unique to your hiring context. Off-the-shelf screening tools optimize for generic job descriptions, not for your specific team's needs.

The Solution

Ertas enables HR teams to fine-tune screening models on their organization's historical hiring data — resumes that were evaluated by experienced recruiters, with outcomes tracked through the hiring funnel. The model learns which qualifications, experience patterns, and skill combinations predict success for each role type at your specific organization, going far beyond keyword matching to understand the semantic meaning of candidate experiences and how they map to your requirements.

With Ertas Studio, talent teams train models on pairs of resumes and structured evaluations — rating each candidate on technical fit, experience relevance, skill alignment, and other dimensions defined by the hiring team. The fine-tuned model then processes new applications and produces structured assessments that match the format and criteria recruiters already use. Deployed through Ertas Cloud with strict access controls, the model serves as a first-pass screening tool that prioritizes the candidate pool, ensuring every qualified candidate gets human attention while reducing the time recruiters spend on clearly mismatched applications. Ertas Vault ensures all candidate data is handled according to data protection regulations.

Key Features

Studio

Custom Evaluation Criteria Training

Train screening models on your specific evaluation rubrics, role requirements, and hiring outcomes using Studio. The model learns your organization's definition of qualification, not a generic one.

Hub

HR and Recruitment Models

Start from models on Hub that understand resume formats, job description terminology, and professional experience patterns — so fine-tuning focuses on your organization-specific criteria.

Cloud

Screening API Integration

Deploy through Cloud as an API that integrates with your ATS. Process applications in real time as they arrive, returning structured evaluations that populate your existing recruiter workflow.

Vault

Candidate Data Privacy

Vault ensures all resume data, evaluation outputs, and training datasets are encrypted and access-controlled. Configurable retention policies support GDPR right-to-deletion requirements.

Example Workflow

A technology company hiring 200 engineers annually receives an average of 500 applications per role. The talent team exports 20,000 historical applications with recruiter evaluations (5-dimension scoring rubric) and hiring outcomes, uploads them to Ertas Vault after removing demographic identifiers, and fine-tunes a screening model in Ertas Studio. The model is deployed as an API connected to their ATS. When new applications arrive, the model produces a structured evaluation matching the recruiter rubric, with a priority score and a brief rationale citing specific resume elements. Recruiters still review every candidate but start with the AI-prioritized list. After three months, the team measures results: recruiter screening time per role drops by 60%, and the percentage of qualified candidates who receive interviews increases by 25% — primarily because the AI surfaces candidates with relevant but non-obvious transferable experience that recruiters previously missed in high-volume manual screening.