{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"patronus-ai","slug":"patronus-ai","name":"Patronus AI","type":"product","url":"https://www.patronus.ai","page_url":"https://unfragile.ai/patronus-ai","categories":["testing-quality","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"patronus-ai__cap_0","uri":"capability://safety.moderation.hallucination.detection.scoring.via.lynx.model","name":"hallucination-detection-scoring-via-lynx-model","description":"Evaluates LLM outputs for factual hallucinations using Patronus's proprietary 70B Lynx model, which claims to outperform GPT-4 on hallucination detection benchmarks. The model analyzes generated text against source documents or ground truth to assign hallucination probability scores, enabling automated quality gates in production pipelines. Scoring is delivered via REST API with configurable thresholds and explanation generation for failed evaluations.","intents":["Detect when my LLM is generating false or unsupported claims in customer-facing outputs","Set up automated rejection of responses with hallucination probability above 0.7","Get explainable scores showing which parts of a response are hallucinated vs grounded"],"best_for":["Enterprise teams deploying LLMs in regulated industries (finance, healthcare, legal)","RAG system builders needing to validate retrieval-augmented responses","QA engineers implementing continuous evaluation in CI/CD pipelines"],"limitations":["Evaluation latency unknown — no SLA or response time documentation provided","Requires ground truth or source documents for comparison; cannot detect hallucinations in open-ended generation without reference material","API pricing at $20 per 1k large evaluator calls adds cost per evaluation; high-volume testing may require budget planning","Lynx model weights not publicly available — evaluation is API-only, no local inference option"],"requires":["Patronus API key (free tier includes $10 credits)","LLM output text to evaluate","Optional: source documents or ground truth for grounded hallucination detection","Network access to Patronus API endpoints"],"input_types":["text (LLM output)","text (source documents or ground truth, optional)","structured JSON (prompt + response pairs)"],"output_types":["hallucination probability score (0-1 float)","explanation text (optional, $10 per 1k calls)","structured evaluation result with pass/fail verdict"],"categories":["safety-moderation","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_1","uri":"capability://safety.moderation.toxicity.and.safety.content.filtering","name":"toxicity-and-safety-content-filtering","description":"Evaluates LLM outputs for toxic language, harmful content, and policy violations using Patronus's safety evaluation models. Integrates with the platform's experiment tracking to flag unsafe responses during development and production monitoring phases. Provides categorical scoring (toxicity level, harm type) and can be configured as a hard gate or soft warning in evaluation pipelines.","intents":["Prevent toxic or abusive LLM outputs from reaching end users","Automatically flag responses that violate brand safety guidelines","Monitor production LLM deployments for drift toward unsafe outputs"],"best_for":["Consumer-facing AI applications (chatbots, content generation, customer service)","Teams operating in regulated markets requiring content moderation audit trails","Platforms with community guidelines needing automated enforcement"],"limitations":["Specific toxicity categories and thresholds not documented — unclear if model detects slurs, hate speech, violence separately or as aggregate score","No information on false positive rates or calibration for different domains (e.g., medical vs casual conversation)","Evaluation cost per call ($10-20 per 1k calls) makes real-time filtering on every response expensive at scale","No local model option — all evaluation requires API calls, creating latency and dependency on Patronus availability"],"requires":["Patronus API key","LLM output text to evaluate","Network connectivity to Patronus API"],"input_types":["text (LLM output)","structured JSON (conversation context, optional)"],"output_types":["toxicity score (0-1 float)","categorical harm labels (e.g., 'hate speech', 'violence', 'abuse')","pass/fail verdict based on configured threshold"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_10","uri":"capability://planning.reasoning.tip.of.the.tongue.task.evaluation.via.blur.model","name":"tip-of-the-tongue-task-evaluation-via-blur-model","description":"Evaluates LLM performance on tip-of-the-tongue (ToT) tasks using Patronus's BLUR model, which assesses the ability to retrieve or infer information when given partial clues or descriptions. BLUR evaluates whether LLMs can correctly identify entities, concepts, or information from vague or incomplete descriptions, measuring retrieval accuracy and reasoning under uncertainty.","intents":["Evaluate my LLM's ability to retrieve information from partial or vague descriptions","Benchmark LLM performance on information retrieval tasks with incomplete context","Assess reasoning quality when dealing with ambiguous or incomplete information"],"best_for":["Teams building search or retrieval systems requiring fuzzy matching and inference","AI researchers studying information retrieval and reasoning under uncertainty","Organizations evaluating LLMs on realistic information retrieval scenarios"],"limitations":["BLUR model and evaluation approach not documented beyond dataset size (573 Q&A pairs)","Unclear how BLUR generalizes beyond the specific 573-pair benchmark dataset","No information on task coverage (entity retrieval, concept identification, etc.)","Evaluation cost ($10-20 per 1k calls) makes large-scale ToT evaluation expensive"],"requires":["Patronus API key","Partial descriptions or clues to test LLM retrieval","Ground truth entities or concepts for comparison"],"input_types":["text (partial description or clue)","structured JSON (ToT task definition)"],"output_types":["retrieval accuracy score (0-1 float)","retrieved entity or concept (text)","confidence score for retrieval"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_11","uri":"capability://data.processing.analysis.dataset.management.and.versioning","name":"dataset-management-and-versioning","description":"Manages evaluation datasets with versioning, allowing teams to track changes to test sets and maintain reproducibility across evaluation runs. Datasets can be uploaded, versioned, and reused across multiple experiments. The platform provides unlimited dataset storage in paid tiers and enables sharing datasets across team members for collaborative evaluation.","intents":["Create and version test datasets for consistent evaluation across model iterations","Share evaluation datasets with team members for collaborative testing","Track dataset changes and maintain reproducibility of evaluation results"],"best_for":["ML teams managing multiple evaluation datasets and versions","Organizations with collaborative evaluation workflows requiring dataset sharing","Teams needing audit trails for dataset changes and evaluation reproducibility"],"limitations":["Dataset format and size limits not documented","Versioning approach not specified — unclear if supporting branching, tagging, or linear versioning","Sharing and access control mechanisms not documented","No information on dataset export or portability"],"requires":["Patronus account","Dataset files (format unknown)","Network access to Patronus platform"],"input_types":["CSV, JSON, or other structured data formats (specific formats unknown)","prompts and expected outputs"],"output_types":["dataset version identifier","dataset metadata (size, creation date, version history)","access logs (who accessed dataset and when)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_12","uri":"capability://automation.workflow.multi.evaluator.chaining.and.aggregation","name":"multi-evaluator-chaining-and-aggregation","description":"Enables chaining multiple evaluators (hallucination, toxicity, PII, brand safety, reasoning quality) in a single evaluation run, with results aggregated and correlated in the experiment dashboard. Evaluators run in parallel or sequence based on configuration, and results are combined to provide holistic quality assessment. Supports custom aggregation logic and filtering based on multiple evaluation criteria.","intents":["Run multiple evaluations on LLM outputs in a single batch operation","Identify correlations between different quality issues (e.g., hallucinations that also leak PII)","Create composite quality scores combining multiple evaluation types"],"best_for":["Teams needing comprehensive LLM evaluation across multiple dimensions","Organizations with complex quality requirements spanning safety, accuracy, and compliance","ML teams analyzing correlations between different failure modes"],"limitations":["Aggregation logic and weighting not documented — unclear how results are combined","Parallel vs. sequential execution not specified — unclear if evaluators run concurrently or serially","Custom aggregation capabilities not documented","Cost of running multiple evaluators in a single run not clearly specified"],"requires":["Patronus API key","LLM outputs to evaluate","Evaluator configuration (which evaluators to run, in what order)"],"input_types":["text (LLM output)","structured JSON (evaluator configuration)"],"output_types":["individual evaluator results (hallucination score, toxicity score, etc.)","aggregated quality score (composite metric)","correlation analysis (relationships between evaluation types)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_13","uri":"capability://data.processing.analysis.analytics.and.reporting.dashboard","name":"analytics-and-reporting-dashboard","description":"Provides web-based dashboards for visualizing evaluation metrics, trends, and performance across experiments. Dashboards display hallucination rates, toxicity scores, PII detection results, and other metrics over time. Supports custom report generation for compliance and stakeholder communication. Analytics are available in Base tier and above, with unlimited comparisons across all tiers.","intents":["Visualize LLM quality metrics and trends over time","Generate compliance reports for regulatory audits","Share evaluation results with stakeholders and executives"],"best_for":["Enterprise teams requiring compliance reporting and audit trails","Organizations with non-technical stakeholders needing quality visibility","Teams analyzing LLM performance trends and improvement opportunities"],"limitations":["Dashboard customization capabilities not documented","Report generation format and export options not specified","Analytics retention policy not documented — unclear if analytics are retained longer than raw logs","Real-time vs. batch analytics not specified"],"requires":["Patronus account (Base tier or above for analytics)","Evaluation data from experiments"],"input_types":["evaluation results from experiments"],"output_types":["web-based dashboard (interactive visualizations)","reports (PDF, CSV, or other formats, unknown)","trend analysis (graphs showing metrics over time)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_2","uri":"capability://safety.moderation.pii.leakage.detection.and.redaction","name":"pii-leakage-detection-and-redaction","description":"Scans LLM outputs for personally identifiable information (PII) including names, email addresses, phone numbers, SSNs, credit card numbers, and other sensitive data. Uses pattern matching and NER-based detection to identify PII in generated text and flag responses that violate data privacy policies. Integrates with Patronus evaluation experiments to prevent PII leakage in production systems.","intents":["Detect when my LLM accidentally includes customer PII in responses","Prevent data privacy violations in regulated industries (healthcare, finance, GDPR compliance)","Audit historical LLM outputs for PII exposure and generate compliance reports"],"best_for":["Healthcare and financial services companies subject to HIPAA, PCI-DSS, GDPR","Customer service AI systems handling sensitive user data","Enterprise teams needing audit trails for data privacy compliance"],"limitations":["PII detection approach (pattern matching vs. contextual NER) not specified — unclear sensitivity/specificity tradeoffs","No documentation on false positive rates or handling of PII-like patterns in non-sensitive contexts (e.g., fictional examples)","Redaction capability not mentioned — evaluation only flags PII, does not automatically remove or mask it","No information on custom PII patterns or domain-specific sensitive data (e.g., medical record numbers, insurance IDs)"],"requires":["Patronus API key","LLM output text to scan","Network access to Patronus API"],"input_types":["text (LLM output)","structured JSON (conversation with metadata)"],"output_types":["PII detection results (list of detected PII entities with type and location)","pass/fail verdict (response contains/does not contain PII)","risk score or severity level"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_3","uri":"capability://safety.moderation.brand.safety.and.policy.compliance.scoring","name":"brand-safety-and-policy-compliance-scoring","description":"Evaluates LLM outputs against brand guidelines and organizational policies to detect off-brand messaging, policy violations, or inappropriate tone. Uses configurable rule sets and semantic matching to identify responses that deviate from brand voice, violate content policies, or contradict organizational guidelines. Results are tracked in the Patronus platform for continuous compliance monitoring.","intents":["Ensure LLM responses align with my brand voice and messaging guidelines","Prevent policy violations in automated customer-facing communications","Monitor production LLMs for drift away from approved messaging"],"best_for":["Marketing and communications teams deploying LLMs for content generation","Enterprise customer service organizations with strict brand guidelines","Regulated industries requiring consistent policy-compliant messaging"],"limitations":["Brand safety evaluation approach not documented — unclear if rule-based, semantic similarity, or LLM-based classification","No information on how custom brand guidelines are specified or updated","Evaluation cost ($10-20 per 1k calls) makes real-time brand checking expensive at scale","No examples of brand safety rules or policy templates provided"],"requires":["Patronus API key","LLM output text to evaluate","Brand guidelines or policy rules (format and specification method unknown)"],"input_types":["text (LLM output)","structured JSON (brand guidelines or policy rules, format unknown)"],"output_types":["brand safety score (0-1 float)","policy violation flags (list of violated policies)","pass/fail verdict"],"categories":["safety-moderation","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_4","uri":"capability://safety.moderation.automated.red.teaming.and.adversarial.testing","name":"automated-red-teaming-and-adversarial-testing","description":"Generates adversarial test cases and attack prompts to probe LLM vulnerabilities, including jailbreak attempts, prompt injection, and edge case scenarios. The platform uses synthetic test generation to create diverse adversarial inputs and evaluates LLM responses against safety and quality criteria. Results are tracked in experiments for regression testing and continuous security monitoring.","intents":["Systematically test my LLM for vulnerabilities before production deployment","Generate adversarial test cases without manual red-teaming effort","Detect regressions in safety and robustness across model updates"],"best_for":["Security-conscious teams deploying LLMs in high-stakes applications","AI safety researchers studying LLM robustness and failure modes","Enterprise teams with limited red-teaming expertise"],"limitations":["Red-teaming approach not documented — unclear if using rule-based generation, LLM-based synthesis, or learned attack patterns","No information on coverage of attack types (jailbreaks, prompt injection, data extraction, etc.)","Scalability of red-teaming not specified — unclear how many adversarial cases are generated per test run","No documentation on customizing red-teaming strategies or attack priorities"],"requires":["Patronus API key","LLM endpoint or model to test","Evaluation criteria (safety thresholds, acceptable response types)"],"input_types":["LLM endpoint URL or model identifier","evaluation criteria (structured JSON)"],"output_types":["adversarial test cases (list of attack prompts)","evaluation results (LLM responses + safety scores)","vulnerability report (list of failed test cases)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_5","uri":"capability://automation.workflow.experiment.tracking.and.comparison.framework","name":"experiment-tracking-and-comparison-framework","description":"Provides a structured experiment management system for tracking LLM evaluation runs, comparing results across model versions, and analyzing performance trends. Experiments capture prompts, model outputs, evaluation scores, and metadata in a queryable database. The platform enables side-by-side comparison of evaluation results and historical trend analysis to detect regressions or improvements.","intents":["Track evaluation results across multiple LLM versions and compare performance","Detect regressions in hallucination, toxicity, or safety scores after model updates","Analyze trends in LLM quality metrics over time"],"best_for":["ML teams managing multiple LLM versions and model iterations","QA engineers implementing continuous evaluation in CI/CD pipelines","Data scientists analyzing LLM performance across different prompts and datasets"],"limitations":["Free tier limited to 2 weeks of experiment history — long-term trend analysis requires paid tier","Comparison capabilities not documented — unclear if supporting statistical significance testing, confidence intervals, or just side-by-side score comparison","Export and integration capabilities unknown — unclear if experiments can be exported to external tools or integrated with CI/CD systems","Scalability not specified — unclear how many experiments or evaluation runs the platform can handle"],"requires":["Patronus account (free tier: 2 projects, 5 experiments per project)","LLM outputs and evaluation results to track","Network access to Patronus platform"],"input_types":["prompts (text)","LLM outputs (text)","evaluation scores (structured JSON)","metadata (model name, version, timestamp, etc.)"],"output_types":["experiment dashboard (web UI)","comparison reports (side-by-side score comparison)","trend analysis (historical performance graphs)","structured data export (format unknown)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_6","uri":"capability://automation.workflow.production.monitoring.and.continuous.evaluation","name":"production-monitoring-and-continuous-evaluation","description":"Monitors production LLM deployments by continuously evaluating outputs against safety and quality criteria. Integrates with production systems to sample or stream LLM responses for real-time evaluation, tracking metrics over time and alerting on anomalies or threshold violations. Provides dashboards for monitoring hallucination rates, toxicity, PII leakage, and brand safety in live systems.","intents":["Monitor my production LLM for quality degradation or safety regressions","Detect when hallucination or toxicity rates exceed acceptable thresholds","Get alerts when PII leakage or brand safety violations occur in production"],"best_for":["Enterprise teams operating LLMs in production with SLAs and compliance requirements","Customer-facing AI applications requiring continuous safety monitoring","Teams needing audit trails for regulatory compliance (GDPR, HIPAA, SOC 2)"],"limitations":["Monitoring architecture not documented — unclear if real-time streaming, batch sampling, or log-based evaluation","Alert configuration and notification channels not specified","Latency impact of evaluation on production systems not documented","Pricing for production monitoring not clearly separated from API pricing — unclear if continuous monitoring incurs additional costs","No information on data retention for production logs beyond free tier's 2-week window"],"requires":["Patronus API key and account (Base tier or above for analytics)","Integration with production LLM system (method unknown)","Evaluation thresholds and alert configuration"],"input_types":["LLM outputs (streamed or sampled from production)","metadata (timestamp, user ID, model version, etc.)"],"output_types":["monitoring dashboard (web UI with real-time metrics)","alerts (email, webhook, or Slack, format unknown)","historical logs (queryable database of evaluated outputs)","compliance reports (audit trails for regulatory requirements)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_7","uri":"capability://automation.workflow.regression.testing.suite.for.model.updates","name":"regression-testing-suite-for-model-updates","description":"Enables systematic regression testing of LLM updates by comparing evaluation results against baseline metrics. Automatically runs evaluation suites on new model versions and flags regressions in hallucination, toxicity, PII, or brand safety scores. Integrates with CI/CD pipelines to gate model deployments based on regression thresholds.","intents":["Automatically test new LLM versions against baseline quality metrics before deployment","Prevent regressions in safety or quality scores from reaching production","Gate model deployments on evaluation thresholds (e.g., hallucination rate < 5%)"],"best_for":["ML teams with continuous model training and frequent update cycles","Enterprise organizations with strict quality gates for production deployments","Teams implementing MLOps practices with automated model validation"],"limitations":["CI/CD integration approach not documented — unclear if supporting GitHub Actions, GitLab CI, Jenkins, or other platforms","Regression detection logic not specified — unclear if using statistical significance testing or simple threshold comparison","No information on customizing regression thresholds or weighting different evaluation types","Baseline management not documented — unclear how baselines are set, updated, or versioned"],"requires":["Patronus API key","CI/CD pipeline integration (method unknown)","Baseline evaluation results for comparison","Regression thresholds and gate criteria"],"input_types":["new model version (endpoint URL or model identifier)","test dataset (prompts and expected outputs)","baseline metrics (previous evaluation results)"],"output_types":["regression report (comparison of new vs baseline scores)","pass/fail verdict (gate decision)","detailed evaluation results (per-prompt scores and explanations)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_8","uri":"capability://planning.reasoning.digital.world.model.simulation.environments","name":"digital-world-model-simulation-environments","description":"Provides synthetic simulation environments for training and evaluating AI agents on realistic task workflows across multiple domains. Environments include research science (literature synthesis), software development (multi-tool workflows), customer service (support cases), product applications (UI navigation), and finance (M&A, trading). Agents interact with simulated tools and data to complete tasks, with evaluation metrics tracking task completion, reasoning quality, and safety.","intents":["Train AI agents on complex multi-step workflows without requiring real data or tools","Evaluate agent reasoning and decision-making in realistic task scenarios","Benchmark agent performance across different domains and task types"],"best_for":["AI researchers studying agent behavior and reasoning in complex environments","Teams developing autonomous agents for specific domains (finance, customer service, software development)","Organizations wanting to evaluate LLMs on realistic task workflows before production deployment"],"limitations":["Simulation fidelity and realism not documented — unclear how closely simulated environments match real-world complexity","Agent interaction model not specified — unclear if agents use natural language, structured APIs, or other interfaces","Customization of simulation environments not documented — unclear if users can create domain-specific simulations","Scalability of simulations not specified — unclear how many concurrent agents or simulation instances are supported","No information on simulation cost or pricing beyond platform tiers"],"requires":["Patronus account with API access","Agent implementation (language and framework unknown)","Task definitions and evaluation criteria"],"input_types":["agent code or endpoint","task definitions (structured JSON)","simulation configuration (environment parameters)"],"output_types":["task completion results (success/failure, steps taken)","evaluation metrics (reasoning quality, safety scores, efficiency)","simulation logs (agent actions, environment state transitions)","performance benchmarks (comparison across agents or domains)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__cap_9","uri":"capability://planning.reasoning.reasoning.chain.evaluation.via.glider.model","name":"reasoning-chain-evaluation-via-glider-model","description":"Evaluates the quality and correctness of LLM reasoning chains using Patronus's GLIDER model, which assesses intermediate reasoning steps and logical flow. Analyzes chain-of-thought outputs to identify reasoning errors, logical inconsistencies, or unsupported conclusions. Provides scores for reasoning quality and can identify where reasoning chains break down or diverge from correct logic.","intents":["Evaluate the quality of chain-of-thought reasoning in my LLM outputs","Identify logical errors or unsupported conclusions in multi-step reasoning","Compare reasoning quality across different LLM models or prompting strategies"],"best_for":["Teams using chain-of-thought prompting and needing to evaluate reasoning quality","AI safety researchers studying LLM reasoning and logical consistency","Organizations deploying LLMs for complex analytical tasks requiring sound reasoning"],"limitations":["GLIDER evaluation approach not documented — unclear if using rule-based logic checking, semantic analysis, or learned reasoning quality assessment","No information on what types of reasoning errors GLIDER can detect (logical fallacies, unsupported claims, circular reasoning, etc.)","Evaluation cost ($10-20 per 1k calls) makes reasoning evaluation expensive for high-volume testing","No documentation on how GLIDER handles different reasoning formats or domains"],"requires":["Patronus API key","Chain-of-thought reasoning text to evaluate","Optional: ground truth or expected reasoning path for comparison"],"input_types":["text (chain-of-thought reasoning)","structured JSON (reasoning steps with intermediate conclusions)"],"output_types":["reasoning quality score (0-1 float)","error identification (list of logical errors or inconsistencies)","step-by-step evaluation (quality score for each reasoning step)","explanation text (optional, additional cost)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"patronus-ai__headline","uri":"capability://safety.moderation.ai.model.evaluation.platform.for.safety.and.quality.assurance","name":"ai model evaluation platform for safety and quality assurance","description":"Patronus AI is an enterprise platform designed to evaluate large language models for hallucination, toxicity, PII leakage, and brand safety, ensuring high-quality AI outputs through automated testing and continuous monitoring.","intents":["best AI model evaluation platform","AI evaluation tool for safety","how to test AI outputs for quality","top platforms for AI model safety assessment","AI evaluation solutions for enterprises"],"best_for":["enterprise AI developers","data scientists","quality assurance teams"],"limitations":["does not generate model outputs","may require technical expertise"],"requires":[],"input_types":["textual outputs from LLMs"],"output_types":["evaluation scores and reports"],"categories":["safety-moderation","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Patronus API key (free tier includes $10 credits)","LLM output text to evaluate","Optional: source documents or ground truth for grounded hallucination detection","Network access to Patronus API endpoints","Patronus API key","Network connectivity to Patronus API","Partial descriptions or clues to test LLM retrieval","Ground truth entities or concepts for comparison","Patronus account","Dataset files (format unknown)"],"failure_modes":["Evaluation latency unknown — no SLA or response time documentation provided","Requires ground truth or source documents for comparison; cannot detect hallucinations in open-ended generation without reference material","API pricing at $20 per 1k large evaluator calls adds cost per evaluation; high-volume testing may require budget planning","Lynx model weights not publicly available — evaluation is API-only, no local inference option","Specific toxicity categories and thresholds not documented — unclear if model detects slurs, hate speech, violence separately or as aggregate score","No information on false positive rates or calibration for different domains (e.g., medical vs casual conversation)","Evaluation cost per call ($10-20 per 1k calls) makes real-time filtering on every response expensive at scale","No local model option — all evaluation requires API calls, creating latency and dependency on Patronus availability","BLUR model and evaluation approach not documented beyond dataset size (573 Q&A pairs)","Unclear how BLUR generalizes beyond the specific 573-pair benchmark dataset","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":null,"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=patronus-ai","compare_url":"https://unfragile.ai/compare?artifact=patronus-ai"}},"signature":"mESO2wwZB+hEOcMjDoenehehguwy8q3Lg2jj5Kso6mBntvKc+LBxbLuO2Vk6cCS1FRINmRBT+kBmEZ8O1bg6CA==","signedAt":"2026-06-22T13:26:50.846Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/patronus-ai","artifact":"https://unfragile.ai/patronus-ai","verify":"https://unfragile.ai/api/v1/verify?slug=patronus-ai","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"}}