{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_brainner","slug":"brainner","name":"Brainner","type":"product","url":"https://www.brainner.ai","page_url":"https://unfragile.ai/brainner","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_brainner__cap_0","uri":"capability://data.processing.analysis.resume.parsing.and.structured.extraction","name":"resume-parsing-and-structured-extraction","description":"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.","intents":["I need to convert hundreds of unstructured resumes into a consistent data format for comparison","I want to automatically identify and tag skills, job titles, and experience duration without manual parsing","I need to handle resumes in different formats and languages without manual preprocessing"],"best_for":["Recruiting teams processing 100+ applications per cycle","Enterprise HR departments with high-volume hiring pipelines","Staffing agencies managing diverse candidate pools"],"limitations":["OCR accuracy degrades on scanned/low-quality PDFs, potentially missing critical qualifications","Struggles with non-standard resume formats or creative layouts that deviate from conventional structure","May misclassify ambiguous terms (e.g., 'Java' as programming language vs. location) without contextual disambiguation","No built-in handling of non-English resumes or region-specific credential formats"],"requires":["Resume documents in PDF, DOCX, or plain text format","Minimum 50-character resume content for reliable extraction","Internet connectivity for cloud-based extraction service"],"input_types":["PDF documents","DOCX files","plain text","scanned images"],"output_types":["structured JSON with extracted fields","normalized skill tags","experience timeline data","education records"],"categories":["data-processing-analysis","document-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_1","uri":"capability://planning.reasoning.ai.driven.candidate.ranking.and.scoring","name":"ai-driven-candidate-ranking-and-scoring","description":"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.","intents":["I want to automatically rank 500 candidates by fit for a specific job opening without reading each resume","I need to identify the top 5% of candidates who match our technical requirements and experience level","I want to surface candidates with non-obvious skill matches that a keyword search would miss"],"best_for":["High-volume hiring roles (customer service, sales, entry-level technical positions)","Teams with limited recruiting bandwidth who need to triage large applicant pools","Organizations hiring for standardized roles with clear, repeatable requirements"],"limitations":["Black-box scoring creates compliance risk under hiring discrimination laws (FCRA, EEOC) — no transparency into which resume features drive ranking decisions","May systematically downrank non-traditional backgrounds (career changers, self-taught developers, international credentials) if training data reflects historical hiring bias","Requires well-defined job descriptions; performs poorly on vague or overly broad role specifications","Cannot account for soft skills, cultural fit, or intangible factors that human reviewers naturally evaluate","Scoring model may overweight recent experience and penalize career gaps, potentially excluding qualified candidates with legitimate employment breaks"],"requires":["Structured job description with required and preferred qualifications","Minimum 10-20 candidate profiles for meaningful ranking differentiation","Historical hiring data (optional) to calibrate scoring weights"],"input_types":["extracted resume data (structured JSON)","job description text","job requirements specification"],"output_types":["ranked candidate list","match scores (0-100 scale)","confidence metrics","matching criteria breakdown"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_2","uri":"capability://automation.workflow.bulk.resume.screening.with.batch.processing","name":"bulk-resume-screening-with-batch-processing","description":"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.","intents":["I need to screen 1000 resumes for a single job opening in under an hour","I want to run screening on all candidates at once rather than processing them individually","I need to re-rank candidates if job requirements change without reprocessing each resume"],"best_for":["Enterprise recruiting teams with high-volume hiring campaigns","Staffing agencies processing large candidate pools across multiple clients","Organizations with seasonal hiring spikes (retail, hospitality, logistics)"],"limitations":["Batch processing introduces latency — results not available in real-time, typically 5-30 minutes depending on volume","Cannot process individual resumes incrementally; requires waiting for full batch completion before results are available","Scaling costs increase linearly with candidate volume; very large batches (10,000+) may incur significant processing fees","No streaming results — recruiters cannot begin reviewing candidates until entire batch completes"],"requires":["Batch of 10+ resumes (minimum for cost-effective processing)","Job description and requirements specification","API access or web interface for batch upload"],"input_types":["resume documents (PDF, DOCX, text)","job description","batch metadata (job ID, hiring manager)"],"output_types":["ranked candidate list (CSV or JSON)","screening report with summary statistics","individual candidate match scores","filtering/sorting options"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_3","uri":"capability://data.processing.analysis.job.description.to.requirements.parsing","name":"job-description-to-requirements-parsing","description":"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.","intents":["I want to automatically extract required skills and experience from a job description without manually creating a requirements checklist","I need to standardize how we define job requirements across different hiring managers and departments","I want to identify missing or ambiguous requirements in a job description before screening begins"],"best_for":["Organizations with inconsistent job description formats across departments","Recruiting teams that want to standardize requirements specification","Companies hiring for similar roles repeatedly and wanting to reuse requirement templates"],"limitations":["Struggles with vague or aspirational language ('rockstar developer', 'self-starter') that doesn't translate to measurable qualifications","May misinterpret nice-to-have requirements as mandatory, or vice versa, if job description doesn't clearly distinguish them","Cannot infer implicit requirements (e.g., 'startup experience' implying flexibility and rapid context-switching) without explicit specification","Requires well-written job descriptions; performs poorly on poorly structured or extremely brief postings"],"requires":["Job description text (minimum 200 characters for reliable parsing)","Job title or role category (optional, improves accuracy)"],"input_types":["job description text","job posting HTML/markdown"],"output_types":["structured requirements JSON","required skills list","experience level specification","education requirements","nice-to-have qualifications"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_4","uri":"capability://automation.workflow.candidate.filtering.and.threshold.configuration","name":"candidate-filtering-and-threshold-configuration","description":"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.","intents":["I want to automatically exclude candidates with match scores below 70% without manually reviewing them","I need to filter out candidates who don't have a specific required skill or certification","I want to set different filtering rules for different job openings based on role criticality"],"best_for":["Recruiting teams with clear, quantifiable qualification thresholds","Organizations hiring for roles with non-negotiable requirements (security clearances, licenses)","High-volume hiring where manual threshold decisions would be time-consuming"],"limitations":["Overly aggressive filtering may eliminate qualified candidates who don't match scoring model assumptions","Cannot account for context-dependent qualifications (e.g., 'Python experience' is critical for data roles but irrelevant for sales)","Threshold configuration requires domain expertise; poorly calibrated thresholds lead to either too many false positives or too many false negatives","No built-in A/B testing or validation to determine optimal threshold values"],"requires":["Ranked candidate list with match scores","Clear definition of minimum acceptable qualifications","Historical hiring data (optional) to calibrate thresholds"],"input_types":["ranked candidate list","filter rules (score thresholds, required skills, experience levels)"],"output_types":["filtered candidate list","filtering statistics (pass/fail counts)","excluded candidate reasons"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_5","uri":"capability://automation.workflow.recruiter.dashboard.and.candidate.review.interface","name":"recruiter-dashboard-and-candidate-review-interface","description":"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.","intents":["I want to quickly scan the top 50 candidates and identify which ones to interview without reading full resumes","I need to compare two candidates side-by-side to decide which one is a better fit","I want to track which candidates I've reviewed and what decisions I've made for compliance purposes"],"best_for":["Recruiting teams using Brainner as their primary screening tool","Organizations that need audit trails of hiring decisions for compliance","Teams with multiple recruiters who need to collaborate on candidate review"],"limitations":["Interface design may not surface important resume details that don't fit into extracted data fields","No built-in collaboration features for multiple recruiters reviewing the same candidates simultaneously","Limited customization of dashboard layout and candidate card information","Audit trail may not capture all decision rationale if recruiters don't explicitly document notes"],"requires":["Web browser with modern JavaScript support","Brainner account with recruiter role permissions","Ranked candidate list from screening process"],"input_types":["ranked candidate data","extracted resume information","match scores and filtering results"],"output_types":["candidate review decisions","interview scheduling data","rejection notes","audit trail logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_6","uri":"capability://safety.moderation.bias.detection.and.fairness.monitoring","name":"bias-detection-and-fairness-monitoring","description":"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.","intents":["I want to check if our screening process is treating candidates from different backgrounds fairly","I need to identify if the AI ranking model is systematically disadvantaging certain demographic groups","I want to generate a fairness report to demonstrate compliance with hiring discrimination laws"],"best_for":["Organizations subject to FCRA or EEOC hiring discrimination regulations","Companies committed to diversity and inclusion in hiring","Enterprises that want to audit AI hiring tools for fairness before deployment"],"limitations":["Demographic inference from resume data (names, education, location) is imprecise and may misclassify candidates, leading to inaccurate bias detection","Statistical significance testing requires large sample sizes; bias detection unreliable for small hiring cohorts","Cannot distinguish between legitimate qualification differences and discriminatory bias without additional context","Fairness monitoring is reactive — identifies bias after screening decisions are made, not preventive","No built-in mechanism to correct or mitigate detected bias; requires manual intervention by recruiters","Legal liability unclear if bias detection fails to identify actual discrimination"],"requires":["Minimum 100+ candidates for statistically meaningful bias analysis","Demographic data (inferred or self-reported) for candidate comparison","Historical hiring outcomes (optional) to validate fairness metrics"],"input_types":["ranked candidate list","screening decisions (pass/fail)","demographic data (inferred or provided)"],"output_types":["fairness report with disparity metrics","demographic breakdown of screening outcomes","statistical significance tests","bias alerts and flags"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_7","uri":"capability://tool.use.integration.ats.integration.and.candidate.data.sync","name":"ats-integration-and-candidate-data-sync","description":"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.","intents":["I want to screen candidates from our ATS without manually exporting and re-importing resume data","I need to push screening results back to our ATS so recruiters can see ranked candidates in their existing workflow","I want to automatically sync candidate status between Brainner and our ATS as hiring decisions are made"],"best_for":["Organizations already using an ATS (Workday, Greenhouse, Lever, etc.)","Recruiting teams that want to integrate Brainner into existing hiring workflows","Enterprises with multiple recruiting tools that need data synchronization"],"limitations":["Integration quality depends on ATS API capabilities; some ATS platforms have limited data export options","Data synchronization latency — changes in one system may not immediately reflect in the other","Requires API credentials and technical setup; not suitable for non-technical recruiters","Limited to ATS platforms with public APIs; proprietary or legacy systems may not be supported","Bidirectional sync can create data conflicts if changes are made in both systems simultaneously"],"requires":["Supported ATS platform (Workday, Greenhouse, Lever, etc.)","ATS API credentials and permissions","Technical setup by IT or recruiting operations team"],"input_types":["candidate data from ATS","job requirements from ATS","resume documents from ATS"],"output_types":["screening results pushed to ATS","candidate rankings in ATS","hiring decision status updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_brainner__cap_8","uri":"capability://planning.reasoning.custom.scoring.model.configuration","name":"custom-scoring-model-configuration","description":"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.","intents":["I want to weight technical skills more heavily than education for engineering roles","I need to train the ranking model on our historical hiring data to improve accuracy for our specific roles","I want to define custom skill categories that are specific to our industry or company"],"best_for":["Organizations with unique hiring criteria that don't fit standard role definitions","Companies with sufficient historical hiring data to train custom models","Enterprises that want to align AI screening with their specific talent strategy"],"limitations":["Requires domain expertise to configure weights and training data; poorly configured models may perform worse than defaults","Training custom models requires significant historical hiring data (typically 100+ hires); small organizations may not have enough data","Model customization increases complexity and maintenance burden; changes to hiring criteria require model retraining","No built-in validation or A/B testing to verify that custom models improve hiring outcomes","Risk of encoding organizational biases into custom models if training data reflects historical hiring discrimination"],"requires":["Historical hiring data (100+ candidates with outcomes) for model training","Domain expertise to define custom weights and skill taxonomies","Technical support from Brainner for model customization"],"input_types":["historical hiring data with outcomes","custom weight specifications","skill taxonomy definitions"],"output_types":["custom-trained ranking model","model performance metrics","feature importance analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Resume documents in PDF, DOCX, or plain text format","Minimum 50-character resume content for reliable extraction","Internet connectivity for cloud-based extraction service","Structured job description with required and preferred qualifications","Minimum 10-20 candidate profiles for meaningful ranking differentiation","Historical hiring data (optional) to calibrate scoring weights","Batch of 10+ resumes (minimum for cost-effective processing)","Job description and requirements specification","API access or web interface for batch upload","Job description text (minimum 200 characters for reliable parsing)"],"failure_modes":["OCR accuracy degrades on scanned/low-quality PDFs, potentially missing critical qualifications","Struggles with non-standard resume formats or creative layouts that deviate from conventional structure","May misclassify ambiguous terms (e.g., 'Java' as programming language vs. location) without contextual disambiguation","No built-in handling of non-English resumes or region-specific credential formats","Black-box scoring creates compliance risk under hiring discrimination laws (FCRA, EEOC) — no transparency into which resume features drive ranking decisions","May systematically downrank non-traditional backgrounds (career changers, self-taught developers, international credentials) if training data reflects historical hiring bias","Requires well-defined job descriptions; performs poorly on vague or overly broad role specifications","Cannot account for soft skills, cultural fit, or intangible factors that human reviewers naturally evaluate","Scoring model may overweight recent experience and penalize career gaps, potentially excluding qualified candidates with legitimate employment breaks","Batch processing introduces latency — results not available in real-time, typically 5-30 minutes depending on volume","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.715Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=brainner","compare_url":"https://unfragile.ai/compare?artifact=brainner"}},"signature":"QTSBte+nKWSb3UGZwzaWz7EJF7FeTJuooB13rIPkvtnN8rkJpCnTdcYxgz7yL/Q7bhJRpqcqifNYT0MYD8/BBg==","signedAt":"2026-06-21T10:11:01.630Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/brainner","artifact":"https://unfragile.ai/brainner","verify":"https://unfragile.ai/api/v1/verify?slug=brainner","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}