{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_hireara","slug":"hireara","name":"HireAra","type":"product","url":"https://www.hireara.ai","page_url":"https://unfragile.ai/hireara","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_hireara__cap_0","uri":"capability://data.processing.analysis.ai.driven.cv.document.parsing.and.structural.extraction","name":"ai-driven cv document parsing and structural extraction","description":"Parses unstructured CV documents (PDF, DOCX, TXT) using machine learning-based document understanding to extract and identify semantic sections (experience, education, skills, contact info) regardless of formatting inconsistencies. Likely uses OCR for scanned PDFs combined with NLP entity recognition to map free-form text into structured fields, enabling downstream standardization without manual field mapping.","intents":["I need to automatically extract candidate information from 500+ CVs with wildly different formats and layouts","I want to convert messy, inconsistently-formatted CVs into machine-readable structured data for downstream processing","I need to identify and preserve key candidate information even when PDFs are scanned or poorly formatted"],"best_for":["Recruitment agencies processing high-volume candidate pipelines with diverse CV formats","HR teams managing applicant tracking systems that require normalized candidate data","Staffing firms needing to batch-process hundreds of submissions daily"],"limitations":["Accuracy degrades on heavily stylized or non-standard CV layouts (e.g., infographic-style CVs, non-Latin scripts)","Scanned PDFs with poor OCR quality may lose information or misclassify sections","No context awareness for ambiguous fields (e.g., distinguishing volunteer work from employment without explicit labels)","Likely requires minimum document quality threshold; extremely corrupted files may fail silently"],"requires":["PDF, DOCX, or TXT file input (typical CV formats)","Internet connectivity for cloud-based ML inference","File size limits (likely 10-50MB per document based on typical SaaS constraints)"],"input_types":["PDF documents","DOCX (Microsoft Word)","TXT (plain text)","potentially RTF or ODT"],"output_types":["structured JSON with extracted fields (name, email, phone, experience, education, skills)","normalized candidate profile objects","confidence scores per extracted field"],"categories":["data-processing-analysis","document-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hireara__cap_1","uri":"capability://text.generation.language.automated.cv.formatting.standardization.and.layout.normalization","name":"automated cv formatting standardization and layout normalization","description":"Applies consistent formatting rules, typography, spacing, and visual hierarchy to parsed CV data, regenerating documents with standardized templates that maintain brand consistency and improve readability. Likely uses template engines (Jinja2, Handlebars) or document generation libraries (ReportLab, LibreOffice) to produce output in PDF or DOCX, ensuring all CVs follow identical visual structure regardless of source format.","intents":["I want all candidate CVs in my pipeline to look professionally consistent when presented to hiring managers","I need to regenerate 200 CVs with our company's standard formatting and branding in one batch operation","I want to ensure CVs have consistent margins, fonts, and section ordering to improve ATS readability"],"best_for":["Recruitment agencies that present candidate CVs to multiple clients and need consistent branding","Large HR departments standardizing candidate materials before forwarding to hiring teams","Staffing firms that want to enforce visual consistency across their candidate database"],"limitations":["Standardization may lose candidate's original design intent or creative formatting choices","Template-based approach limits customization per candidate (e.g., cannot preserve unique visual branding from original CV)","Output quality depends on template design; poor templates produce poor-looking CVs regardless of content quality","May strip or reformat non-standard content (e.g., custom graphics, charts, or unconventional section types)"],"requires":["Parsed CV data in structured format (output from parsing capability)","Template library or design system for CV layouts","Document generation backend (likely cloud-based for scalability)"],"input_types":["structured candidate data (JSON/objects from parsing step)","optional: branding guidelines (colors, fonts, logos)"],"output_types":["PDF documents (primary output)","DOCX documents (secondary, for editability)","HTML preview (for web-based review)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hireara__cap_2","uri":"capability://data.processing.analysis.ats.compatibility.optimization.and.keyword.enhancement","name":"ats compatibility optimization and keyword enhancement","description":"Analyzes CV content against known ATS parsing rules and job description keywords, suggesting or automatically inserting relevant terms, restructuring sections for optimal parsing, and removing formatting elements that confuse ATS systems (tables, graphics, special characters). Uses keyword extraction and semantic matching to identify gaps between candidate qualifications and job requirements, then enhances CV text to improve ATS match scores without misrepresenting candidate experience.","intents":["I want to ensure candidate CVs pass through ATS filters and reach human recruiters","I need to identify which candidates are keyword-matched to specific job openings and highlight those matches","I want to automatically improve CV ATS compatibility without manually rewriting each document"],"best_for":["Recruitment agencies managing high-volume pipelines where ATS filtering is a bottleneck","In-house HR teams using ATS systems that filter candidates before human review","Staffing firms that need to match candidates to multiple job openings simultaneously"],"limitations":["Over-optimization for ATS may produce keyword-stuffed CVs that read poorly to human reviewers","Keyword matching is semantic and may miss domain-specific terminology or acronyms not in training data","Cannot add qualifications or experience that don't exist; only reorganizes and emphasizes existing content","ATS rules vary widely across systems; optimization for one ATS may not transfer to another","Requires job description input for keyword matching; generic optimization without job context is less effective"],"requires":["Parsed CV data (structured candidate information)","Optional: job description or job posting text for keyword matching","Access to ATS compatibility rules database (proprietary or industry-standard)"],"input_types":["structured candidate data (JSON)","job description text (optional, for targeted optimization)","ATS system identifier (optional, to apply system-specific rules)"],"output_types":["enhanced CV text with keyword suggestions","ATS compatibility score (0-100)","list of missing keywords or skills","restructured section ordering for optimal ATS parsing"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hireara__cap_3","uri":"capability://automation.workflow.batch.cv.processing.and.bulk.formatting.workflow","name":"batch cv processing and bulk formatting workflow","description":"Orchestrates end-to-end CV processing for multiple documents in parallel, managing job queues, error handling, and progress tracking across parsing, standardization, and optimization steps. Implements asynchronous processing with retry logic, timeout handling, and partial failure recovery, allowing recruiters to upload 50-500+ CVs and receive formatted outputs without manual intervention per document.","intents":["I need to process 300 CVs from a job fair or bulk application batch in under an hour","I want to upload a folder of CVs and get back standardized, ATS-optimized versions without clicking through each one","I need visibility into processing progress and error handling for failed documents"],"best_for":["Recruitment agencies handling seasonal hiring surges or large-scale recruitment campaigns","HR departments processing bulk applications from job fairs or university recruiting events","Staffing firms that need to rapidly onboard new candidate batches into their system"],"limitations":["Processing latency scales with batch size; 500 CVs may take 10-30 minutes depending on system load","Failed documents may require manual intervention; no automatic retry with format conversion","Batch processing is asynchronous, so real-time feedback per document is limited","Storage and processing costs scale with batch size; very large batches (1000+) may incur additional fees","No built-in deduplication; duplicate CVs from same candidate are processed separately"],"requires":["Web UI or API endpoint for batch upload","Cloud infrastructure for parallel processing (likely containerized workers)","Message queue system (e.g., Celery, RabbitMQ) for job orchestration","Storage backend for input/output documents (S3, GCS, or similar)"],"input_types":["ZIP file containing multiple CVs","folder upload via web UI","API endpoint for programmatic batch submission"],"output_types":["ZIP file with formatted CVs","CSV report with processing status per document","JSON API response with document URLs and metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hireara__cap_4","uri":"capability://data.processing.analysis.cv.presentation.quality.assessment.and.readability.scoring","name":"cv presentation quality assessment and readability scoring","description":"Analyzes CV documents for readability, visual hierarchy, and presentation quality using metrics like font consistency, whitespace distribution, section clarity, and information density. Generates a readability score (0-100) and provides specific recommendations for improvement (e.g., 'reduce font size variation', 'increase margins', 'break up dense paragraphs'). Likely uses computer vision techniques to analyze PDF/image layouts and NLP to assess text clarity and conciseness.","intents":["I want to identify which candidate CVs are poorly formatted and need improvement before presenting to hiring managers","I need to score CVs on presentation quality to ensure consistency across my candidate pipeline","I want automated feedback to help candidates improve their CV presentation without manual review"],"best_for":["Recruitment agencies that want to maintain quality standards before presenting candidates","HR teams using CV quality as a proxy for candidate attention to detail","Staffing firms that provide CV coaching or feedback to candidates"],"limitations":["Readability scoring is subjective; metrics may not align with hiring manager preferences","Computer vision analysis of PDFs can be fragile with complex layouts or non-standard fonts","Recommendations are generic (e.g., 'improve spacing') and may not account for industry-specific CV norms","Cannot assess content quality or accuracy, only presentation; a well-formatted CV with false claims scores high","Cultural bias risk: scoring may penalize non-Western CV formats or unconventional layouts that are normal in other regions"],"requires":["CV document in PDF or image format","Readability scoring model (trained on human-rated CVs or industry standards)"],"input_types":["PDF documents","rendered CV images (PNG, JPG)"],"output_types":["readability score (0-100)","list of specific improvement recommendations","visual heatmap highlighting problem areas (optional)","comparison to industry benchmarks"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hireara__cap_5","uri":"capability://automation.workflow.multi.format.cv.export.and.version.management","name":"multi-format cv export and version management","description":"Generates and manages multiple output formats (PDF, DOCX, HTML, plain text) from a single standardized CV representation, allowing recruiters to export CVs in format-specific optimizations. Maintains version history of CV transformations, enabling rollback to previous formats or comparison between original and standardized versions. Implements format-specific optimizations (e.g., PDF for printing/archival, DOCX for editing, HTML for web preview).","intents":["I need to export candidate CVs in different formats depending on where they're being sent (PDF for clients, DOCX for internal editing, HTML for web portal)","I want to maintain a version history of CV changes so I can track what was modified and revert if needed","I need to generate a web-friendly HTML version of a CV for candidate profiles on our recruitment website"],"best_for":["Recruitment agencies that distribute CVs to multiple clients in different formats","HR teams that need both archival (PDF) and editable (DOCX) versions of standardized CVs","Staffing firms with candidate portals that display CVs in multiple formats"],"limitations":["Format conversion may lose formatting fidelity (e.g., DOCX to PDF may render differently across systems)","Version history storage adds database overhead; very long histories (100+ versions) may slow retrieval","HTML export may not preserve all styling from PDF/DOCX; browser compatibility issues possible","Plain text export loses all formatting; suitable only for ATS systems or text-based processing","No built-in collaboration or commenting; version history is read-only"],"requires":["Standardized CV data structure (output from formatting step)","Document generation libraries for each format (ReportLab for PDF, python-docx for DOCX, Jinja2 for HTML)","Version control system or database for tracking changes"],"input_types":["structured CV data (JSON/objects)"],"output_types":["PDF file","DOCX file","HTML file","plain text file","version metadata (timestamp, format, change summary)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hireara__cap_6","uri":"capability://data.processing.analysis.candidate.profile.enrichment.and.skill.normalization","name":"candidate profile enrichment and skill normalization","description":"Extracts and normalizes candidate skills, experience, and qualifications from CV text, mapping them to standardized skill taxonomies or industry-standard competency frameworks (e.g., ESCO, O*NET). Enriches candidate profiles with inferred skills based on job titles, education, and explicit mentions, enabling downstream skill-based matching and gap analysis. Uses NLP entity recognition and semantic similarity to identify skill synonyms and variations (e.g., 'Python programming', 'Python development', 'Py' all map to 'Python').","intents":["I want to extract all skills from a CV and normalize them to a standard skill taxonomy so I can match candidates to job requirements","I need to identify skill gaps between a candidate's qualifications and a job opening","I want to infer skills that candidates have but didn't explicitly mention (e.g., inferring 'project management' from 'led team of 5 engineers')"],"best_for":["Recruitment agencies that need skill-based candidate matching across multiple job openings","HR teams using skill inventories to identify internal mobility opportunities","Staffing firms that want to build searchable skill profiles for their candidate database"],"limitations":["Skill normalization depends on quality of underlying taxonomy; missing or outdated skills in taxonomy reduce coverage","Skill inference is probabilistic; false positives possible (e.g., inferring 'leadership' from 'worked in team' without actual leadership role)","Synonym mapping may miss domain-specific variations or emerging skill names not in training data","Cannot distinguish skill proficiency levels from CV text alone; all extracted skills treated equally","Requires job description or skill taxonomy input for effective matching; generic skill extraction is less useful"],"requires":["Parsed CV data with experience and education sections","Skill taxonomy or competency framework (proprietary or industry-standard like ESCO)","NLP model trained on skill extraction and synonym mapping"],"input_types":["structured CV data (JSON with experience, education, skills sections)","optional: job description for targeted skill matching"],"output_types":["normalized skill list with confidence scores","skill proficiency levels (if detectable from CV text)","inferred skills with reasoning","skill gaps vs. job requirements (if job description provided)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["PDF, DOCX, or TXT file input (typical CV formats)","Internet connectivity for cloud-based ML inference","File size limits (likely 10-50MB per document based on typical SaaS constraints)","Parsed CV data in structured format (output from parsing capability)","Template library or design system for CV layouts","Document generation backend (likely cloud-based for scalability)","Parsed CV data (structured candidate information)","Optional: job description or job posting text for keyword matching","Access to ATS compatibility rules database (proprietary or industry-standard)","Web UI or API endpoint for batch upload"],"failure_modes":["Accuracy degrades on heavily stylized or non-standard CV layouts (e.g., infographic-style CVs, non-Latin scripts)","Scanned PDFs with poor OCR quality may lose information or misclassify sections","No context awareness for ambiguous fields (e.g., distinguishing volunteer work from employment without explicit labels)","Likely requires minimum document quality threshold; extremely corrupted files may fail silently","Standardization may lose candidate's original design intent or creative formatting choices","Template-based approach limits customization per candidate (e.g., cannot preserve unique visual branding from original CV)","Output quality depends on template design; poor templates produce poor-looking CVs regardless of content quality","May strip or reformat non-standard content (e.g., custom graphics, charts, or unconventional section types)","Over-optimization for ATS may produce keyword-stuffed CVs that read poorly to human reviewers","Keyword matching is semantic and may miss domain-specific terminology or acronyms not in training data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"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":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=hireara","compare_url":"https://unfragile.ai/compare?artifact=hireara"}},"signature":"5jjjC38TwunAYXjQ9G3sqmXcuY5km2TaQHgA1jILd2ZU07efdzbh1NVIkkfMZ4c86jMpCWGdln/97DS7SfJLDw==","signedAt":"2026-06-21T13:23:26.161Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hireara","artifact":"https://unfragile.ai/hireara","verify":"https://unfragile.ai/api/v1/verify?slug=hireara","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"}}