Brainner

Automatically extracts and structures resume content (skills, experience, education, certifications) from unformatted documents using OCR and NLP-based entity recognition. The system converts free-form resume text into a normalized, queryable data model that enables downstream ranking and filtering operations. This extraction layer handles multiple resume formats (PDF, DOCX, plain text) and standardizes inconsistent terminology across candidate profiles.

Ranks candidates against job requirements using a learned scoring model that weights extracted resume features (skills match, experience level, education, tenure patterns) against job description criteria. The system likely uses embedding-based semantic matching or learned ranking models to identify candidates whose profiles align with role requirements, producing a ranked list with confidence scores. This enables recruiters to focus on top-matched candidates without manual review of all applications.

Processes large volumes of resumes (hundreds to thousands) in parallel, applying parsing, extraction, and ranking operations across the entire applicant pool in a single batch job. The system likely uses asynchronous job queuing and distributed processing to handle high-throughput screening without blocking user interactions. Results are aggregated and presented as ranked candidate lists, enabling recruiters to review screening outcomes for an entire job opening at once.

Automatically extracts and normalizes job requirements from free-form job descriptions, identifying required skills, experience levels, education credentials, and role-specific qualifications. The system converts unstructured job posting text into a structured requirements specification that serves as the matching criteria for candidate ranking. This enables consistent evaluation across multiple candidates even if job descriptions are written in different styles or formats.

Allows recruiters to set custom filtering thresholds and rules to automatically exclude candidates below specified match scores or lacking critical qualifications. The system applies these filters to the ranked candidate list, surfacing only candidates who meet minimum criteria. This enables recruiters to define what 'qualified' means for their specific role and automatically eliminate candidates who don't meet those standards, reducing manual review burden.

Provides a web-based interface for recruiters to view ranked candidate lists, review extracted resume data, apply custom filters, and make hiring decisions. The dashboard displays candidate match scores, key qualifications, and extracted resume information in an organized, scannable format. Recruiters can drill down into individual candidate profiles, compare candidates side-by-side, and mark candidates for next-stage interviews or rejection, creating an audit trail of screening decisions.

Monitors screening outcomes for potential demographic bias by analyzing whether candidates from different demographic groups (inferred from names, education, or other signals) are ranked or filtered differently. The system may flag screening results that show statistically significant disparities in pass rates across demographic groups, alerting recruiters to potential fairness issues. This capability aims to provide transparency into potential bias in the AI ranking model, though the effectiveness depends on the accuracy of demographic inference and the statistical methods used.

Integrates with popular Applicant Tracking Systems (ATS) via APIs or data import/export to synchronize candidate data, screening results, and hiring decisions between Brainner and the ATS. The system can import candidate resumes and job requirements from the ATS, run screening, and push results back to the ATS for recruiter review and next-stage actions. This integration reduces manual data entry and keeps candidate information synchronized across tools.

Allows organizations to customize how the AI ranking model weights different resume features (skills, experience, education, tenure patterns) to match their specific hiring priorities. The system may provide interfaces to adjust weights for different qualifications, define custom skill taxonomies, or train models on historical hiring data specific to the organization. This enables organizations to tailor the ranking model to their unique hiring criteria rather than using a generic, one-size-fits-all model.