{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_jobtitlesai","slug":"jobtitlesai","name":"JobtitlesAI","type":"product","url":"https://jobtitlesai.com","page_url":"https://unfragile.ai/jobtitlesai","categories":["automation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_jobtitlesai__cap_0","uri":"capability://data.processing.analysis.multilingual.job.title.standardization.with.ml.classification","name":"multilingual job title standardization with ml classification","description":"Accepts raw job titles in multiple languages and applies trained machine learning models to map them to standardized job classifications, handling linguistic variations, regional naming conventions, and language-specific terminology. The system likely uses transformer-based embeddings or fine-tuned language models to understand semantic similarity across languages, enabling cross-lingual job title normalization without requiring separate models per language pair.","intents":["I need to consolidate job titles from acquisitions across 5 countries into a single taxonomy for reporting","Our HR system has 200+ variations of 'Software Engineer' titles and I need to deduplicate them programmatically","I'm building a global talent marketplace and need to normalize job titles submitted in French, German, Spanish, and English","I need to map legacy job titles from our European subsidiary to our US job classification system automatically"],"best_for":["Mid-market HR teams managing multilingual workforces across 3+ countries","Talent acquisition platforms handling job postings in multiple languages","Organizations consolidating job data after M&A with international targets","Global recruitment agencies needing standardized job title taxonomy across regions"],"limitations":["Classification accuracy degrades for highly specialized or emerging role titles (e.g., 'Prompt Engineer', 'AI Safety Researcher') where training data is sparse","No transparency on underlying classification standard (ESCO, O*NET, proprietary) limits data portability and compliance verification","Language support likely limited to major languages (EN, FR, DE, ES, IT) with reduced accuracy for minority or low-resource languages","Cannot handle context-dependent titles without additional metadata (e.g., 'Manager' could mean 5+ different roles depending on industry)"],"requires":["Raw job title text input (minimum 1-3 words per title)","Language identifier or auto-detection capability (ISO 639-1 codes preferred)","API key or authentication token for freemium/paid tier access","Network connectivity for cloud-based ML inference"],"input_types":["plain text (single job title)","CSV/JSON batch (multiple titles with metadata)","unstructured text (job descriptions with title extraction)"],"output_types":["standardized job title (string)","classification code (ESCO/O*NET/proprietary identifier)","confidence score (0-1 probability)","alternative matches (ranked list of similar standardized titles)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jobtitlesai__cap_1","uri":"capability://data.processing.analysis.batch.job.title.classification.with.confidence.scoring","name":"batch job title classification with confidence scoring","description":"Processes multiple job titles in a single API request, returning standardized classifications with confidence scores for each match. The system likely implements batching optimizations to amortize ML model loading costs and may use caching or trie-based lookups for common titles to reduce latency, enabling efficient processing of large HR datasets without per-title API overhead.","intents":["I have 10,000 job titles from our HRIS system and need to classify them all in one operation","I want to identify which job titles in our database have low confidence matches so I can manually review them","I need to export a CSV with original titles, standardized titles, and confidence scores for audit purposes","I'm building a data pipeline that runs nightly to reclassify new job titles added to our system"],"best_for":["HR teams with large existing job title datasets (1,000+ titles) needing bulk standardization","Data engineers building ETL pipelines for talent data consolidation","Compliance officers needing audit trails of classification decisions with confidence metrics","Analytics teams analyzing job market trends across standardized title categories"],"limitations":["Batch processing may have rate limits or queue delays during peak usage (typical SaaS constraint)","Confidence scores are model-dependent and not calibrated against human expert agreement rates","No built-in handling of context (department, company size, industry) that could improve classification accuracy","Batch results lack explainability — no indication of which features or training examples influenced each classification decision"],"requires":["CSV, JSON, or API-compatible format with job title column","Batch size limits (likely 100-10,000 titles per request depending on tier)","API authentication and rate limit quota","Structured metadata optional but recommended (industry, country, department)"],"input_types":["CSV with job_title column","JSON array of title strings or objects","API POST request with batch payload"],"output_types":["JSON with standardized_title, classification_code, confidence_score per input","CSV export with original + standardized titles","structured data with alternative matches ranked by confidence"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jobtitlesai__cap_2","uri":"capability://tool.use.integration.job.title.api.with.real.time.single.title.classification","name":"job title api with real-time single-title classification","description":"Exposes a REST or GraphQL API endpoint that accepts a single job title and returns its standardized classification in real-time, enabling integration into HR systems, job posting platforms, and talent management workflows. The API likely implements request caching and CDN distribution to minimize latency for frequently-classified titles, with response times optimized for synchronous user-facing workflows.","intents":["I want to auto-classify job titles as they're entered into our job posting form before submission","I need to integrate job title standardization into our ATS so new hires are automatically classified on hire","I'm building a job search feature and want to normalize user-submitted job titles to our taxonomy in real-time","I need to call the classification API from our internal tools to standardize titles on-the-fly"],"best_for":["Product teams building job posting or talent management platforms","HR software vendors integrating standardization as a feature","Job search and matching platforms normalizing user input","Internal tools and dashboards requiring real-time title classification"],"limitations":["Real-time API calls add latency (likely 100-500ms per request) compared to batch processing, making it unsuitable for high-throughput scenarios","No context awareness — single title classification without department, industry, or company size metadata limits accuracy","Rate limiting on freemium tier may throttle high-volume integrations (typical SaaS constraint)","No webhook or async callback mechanism for long-running classification requests"],"requires":["API key or OAuth token for authentication","HTTP/REST or GraphQL client capability","Network connectivity with acceptable latency tolerance (100-500ms)","Error handling for rate limit (429) and service unavailability (5xx) responses"],"input_types":["job title string (plain text, 1-100 characters)","optional language code (ISO 639-1)","optional metadata (industry, country, company_size)"],"output_types":["standardized job title (string)","classification code (identifier)","confidence score (0-1)","alternative matches (array of similar titles)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jobtitlesai__cap_3","uri":"capability://automation.workflow.freemium.tier.with.limited.classification.quota.for.validation","name":"freemium tier with limited classification quota for validation","description":"Offers a free tier with restricted API quota (likely 100-1,000 classifications per month) enabling HR teams to test classification accuracy on their actual job title data before committing to paid plans. The freemium model uses quota-based rate limiting and likely includes basic analytics (classification distribution, confidence histogram) to help teams evaluate fit before purchase.","intents":["I want to test JobtitlesAI on our actual job titles to see if it works for our use case before paying","I need to classify a small dataset (50-200 titles) for a one-time project without committing to a subscription","I want to evaluate multiple job title classification tools and need free tier access to compare accuracy","I'm a solo HR consultant and need occasional job title standardization without monthly subscription costs"],"best_for":["Small HR teams (1-50 employees) with occasional classification needs","Solo HR consultants or contractors evaluating tools","Organizations piloting job title standardization before full rollout","Developers prototyping HR integrations and needing free API access for testing"],"limitations":["Quota limits (likely 100-1,000 classifications/month) insufficient for large-scale HR operations","No batch processing on free tier — likely limited to single-title API calls only","Limited analytics or reporting features compared to paid tiers","No SLA or priority support, making it unsuitable for production workloads"],"requires":["Email signup or account creation (no credit card required)","Monthly quota reset (likely on calendar month or subscription anniversary)","Acceptance of freemium terms (data usage, feature limitations)"],"input_types":["job title string via web UI or API"],"output_types":["standardized title with confidence score","basic usage analytics (classifications used, remaining quota)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jobtitlesai__cap_4","uri":"capability://data.processing.analysis.job.title.confidence.based.filtering.and.manual.review.workflow","name":"job title confidence-based filtering and manual review workflow","description":"Provides confidence scores for each classification and enables HR teams to filter results by confidence threshold, automatically routing low-confidence matches to manual review queues. The system likely implements a dashboard or export feature showing classifications grouped by confidence bands (high: 0.9+, medium: 0.7-0.9, low: <0.7), enabling risk-aware workflows where high-confidence matches are auto-accepted and low-confidence matches are escalated for human review.","intents":["I want to auto-accept job title classifications with >90% confidence and manually review anything below that threshold","I need to identify which job titles in our system have ambiguous or uncertain classifications","I want to export a report showing which titles were confidently classified vs. which need manual review","I'm building a workflow where low-confidence classifications trigger a task for our HR team to verify"],"best_for":["HR teams with compliance or audit requirements needing to track classification decisions","Organizations implementing gradual rollout of automated classification with human oversight","Data quality teams building validation workflows for job title standardization","Compliance officers needing to demonstrate human review of automated decisions"],"limitations":["Confidence thresholds are model-dependent and not calibrated against human expert agreement — no guarantee that 90% confidence = 90% accuracy","No explanation of why a classification has low confidence (e.g., ambiguous title, rare job market, training data gap)","Manual review workflow is not built-in — requires integration with external task management or HR systems","Confidence scores may be overconfident or underconfident depending on model calibration and training data distribution"],"requires":["Access to confidence scores in API responses or batch export","Dashboard or reporting interface to view confidence distribution","Optional: integration with task management system (Jira, Asana, etc.) for manual review workflow","HR team capacity for manual review of flagged titles"],"input_types":["classified job titles with confidence scores"],"output_types":["filtered results by confidence threshold","confidence distribution histogram or report","export of low-confidence matches for manual review","audit trail of classification decisions with confidence metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jobtitlesai__cap_5","uri":"capability://data.processing.analysis.job.title.synonym.and.variant.detection.across.languages","name":"job title synonym and variant detection across languages","description":"Identifies and groups job title variants and synonyms across multiple languages, recognizing that 'Software Engineer', 'Software Developer', 'Programmer', and 'Développeur Logiciel' (French) all map to the same standardized role. The system likely uses semantic similarity matching (embeddings-based) combined with linguistic rule-based matching to handle both exact synonyms and regional naming conventions without requiring manual synonym dictionaries.","intents":["I need to consolidate 50 variations of 'Product Manager' titles from our global offices into a single standardized title","I want to understand which job titles in our system are synonyms or variants of the same role","I'm building a job search feature and need to match user queries to standardized titles even if they use different terminology","I need to identify all variations of 'Data Scientist' across our organization (Data Scientist, Data Science Engineer, ML Engineer, etc.)"],"best_for":["Organizations with messy, inconsistent job title data from multiple sources or acquisitions","Global companies with regional job title variations (e.g., 'Consultant' in UK vs 'Senior Analyst' in US)","Job search and matching platforms needing fuzzy matching on job titles","Data quality teams building job title deduplication workflows"],"limitations":["Synonym detection may produce false positives for titles that sound similar but have different responsibilities (e.g., 'Manager' could mean people manager or project manager)","Requires sufficient training data for each language pair — less accurate for minority languages or emerging roles","No domain-specific synonym handling — cannot distinguish industry-specific variants (e.g., 'Engineer' means different things in software vs. civil engineering)","Synonym grouping is not customizable — cannot add organization-specific synonyms or override default groupings"],"requires":["Job title text input with language identifiers","Multilingual embeddings model (mBERT, XLM-RoBERTa, or similar)","Similarity threshold configuration (likely 0.7-0.9 for grouping variants)"],"input_types":["job title strings in multiple languages","optional language codes (ISO 639-1)"],"output_types":["grouped variants with canonical/standardized title","similarity scores between variants","alternative matches ranked by semantic similarity"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jobtitlesai__cap_6","uri":"capability://data.processing.analysis.job.classification.standard.mapping.esco.o.net.proprietary","name":"job classification standard mapping (esco/o*net/proprietary)","description":"Maps standardized job titles to recognized job classification standards such as ESCO (European Skills/Competences, Qualifications and Occupations), O*NET (US Occupational Information Network), or proprietary taxonomy. The system likely maintains mappings between multiple standards, enabling organizations to export classifications in their preferred format or standard for compliance, reporting, or data portability purposes.","intents":["I need to map our job titles to ESCO codes for EU compliance and skills reporting","I want to export job classifications in O*NET format for integration with US government labor data","I need to understand which ESCO competencies are required for each job title in our organization","I'm building a skills-based job matching system and need to map titles to standardized competency frameworks"],"best_for":["European HR teams requiring ESCO compliance for skills reporting and labor market analysis","US organizations needing O*NET integration for government reporting or labor market data","Skills-based HR platforms building competency frameworks","Organizations requiring data portability across multiple HR systems with different classification standards"],"limitations":["Mapping between standards (ESCO ↔ O*NET) is lossy — not all titles have direct equivalents across standards","Proprietary taxonomy mappings are opaque — no transparency on how JobtitlesAI's internal classifications map to public standards","Standard coverage may be incomplete — emerging roles (e.g., 'Prompt Engineer', 'AI Safety Researcher') may not exist in ESCO or O*NET","Mappings are static and updated infrequently — may lag behind evolving job market and new role definitions"],"requires":["Selection of target classification standard (ESCO, O*NET, or proprietary)","Understanding of standard structure and codes (e.g., ESCO 8-digit codes, O*NET SOC codes)","Optional: mapping configuration or custom standard support"],"input_types":["standardized job title from JobtitlesAI classification"],"output_types":["ESCO code and title (e.g., '2511.1000 - Software developer')","O*NET SOC code and title (e.g., '15-1132.00 - Software Developers, Applications')","proprietary taxonomy code and description","competency or skill mappings associated with the standard"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Raw job title text input (minimum 1-3 words per title)","Language identifier or auto-detection capability (ISO 639-1 codes preferred)","API key or authentication token for freemium/paid tier access","Network connectivity for cloud-based ML inference","CSV, JSON, or API-compatible format with job title column","Batch size limits (likely 100-10,000 titles per request depending on tier)","API authentication and rate limit quota","Structured metadata optional but recommended (industry, country, department)","API key or OAuth token for authentication","HTTP/REST or GraphQL client capability"],"failure_modes":["Classification accuracy degrades for highly specialized or emerging role titles (e.g., 'Prompt Engineer', 'AI Safety Researcher') where training data is sparse","No transparency on underlying classification standard (ESCO, O*NET, proprietary) limits data portability and compliance verification","Language support likely limited to major languages (EN, FR, DE, ES, IT) with reduced accuracy for minority or low-resource languages","Cannot handle context-dependent titles without additional metadata (e.g., 'Manager' could mean 5+ different roles depending on industry)","Batch processing may have rate limits or queue delays during peak usage (typical SaaS constraint)","Confidence scores are model-dependent and not calibrated against human expert agreement rates","No built-in handling of context (department, company size, industry) that could improve classification accuracy","Batch results lack explainability — no indication of which features or training examples influenced each classification decision","Real-time API calls add latency (likely 100-500ms per request) compared to batch processing, making it unsuitable for high-throughput scenarios","No context awareness — single title classification without department, industry, or company size metadata limits accuracy","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":"2026-05-24T12:16:31.445Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=jobtitlesai","compare_url":"https://unfragile.ai/compare?artifact=jobtitlesai"}},"signature":"1Oyq70r/pXL8TrUFtHoAP9SEJ8iyGGS9n81kRitj8vntSZY8B1fJqrK/IfQmObR3KZ2+8JQJXcOGYOSnfPOJAQ==","signedAt":"2026-06-21T10:32:28.049Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jobtitlesai","artifact":"https://unfragile.ai/jobtitlesai","verify":"https://unfragile.ai/api/v1/verify?slug=jobtitlesai","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"}}