{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_aporia","slug":"aporia","name":"Aporia","type":"product","url":"https://www.aporia.com","page_url":"https://unfragile.ai/aporia","categories":["code-review-security"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_aporia__cap_0","uri":"capability://monitoring.real.time.model.output.anomaly.detection","name":"real-time model output anomaly detection","description":"Monitors LLM outputs in production to identify unusual patterns, hallucinations, and unexpected model behavior as they occur. Detects deviations from baseline performance without requiring model retraining or manual rule definition.","intents":["I need to catch when my LLM starts producing incorrect or nonsensical outputs","I want to detect hallucinations in real-time before they reach users","I need to know immediately when model behavior changes unexpectedly"],"best_for":["ML engineers","data scientists","production teams"],"limitations":["Requires baseline data to establish normal behavior patterns","Effectiveness depends on quality of training data","May generate false positives in early deployment phases"],"requires":["Production LLM deployment","Historical baseline data","API integration with model serving infrastructure"],"input_types":["LLM outputs","model predictions","inference logs"],"output_types":["anomaly alerts","severity scores","flagged outputs"],"categories":["monitoring","AI safety"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_1","uri":"capability://monitoring.data.drift.and.distribution.shift.monitoring","name":"data drift and distribution shift monitoring","description":"Continuously tracks changes in input data distributions and model feature spaces to identify when production data diverges from training data. Alerts teams to potential performance degradation caused by data drift.","intents":["I need to know when my production data is different from what the model was trained on","I want to detect gradual performance decline caused by changing user inputs","I need to understand why model accuracy is dropping over time"],"best_for":["ML engineers","data scientists","MLOps teams"],"limitations":["Requires sufficient historical data to establish baseline distributions","May not detect subtle shifts in semantic meaning","Needs regular calibration for seasonal or expected data changes"],"requires":["Production inference pipeline","Training data statistics","Continuous data logging"],"input_types":["production input features","inference logs","historical training data"],"output_types":["drift alerts","distribution comparisons","statistical reports"],"categories":["monitoring","data quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_10","uri":"capability://safety.guardrail.policy.configuration.and.enforcement","name":"guardrail policy configuration and enforcement","description":"Allows definition and enforcement of safety guardrails and business rules on model outputs. Enables teams to specify what outputs are acceptable and automatically filter or flag violations.","intents":["I want to prevent my LLM from generating harmful, biased, or inappropriate content","I need to enforce business rules on model outputs automatically","I want to ensure outputs comply with my company's content policies"],"best_for":["ML engineers","safety teams","content policy teams"],"limitations":["Requires clear policy definition","May generate false positives","Needs regular updates as policies evolve"],"requires":["Policy definitions","Content classification rules","Output filtering infrastructure"],"input_types":["model outputs","policy rules","content guidelines"],"output_types":["filtered outputs","policy violation flags","enforcement logs"],"categories":["safety","governance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_11","uri":"capability://pricing.freemium.tier.evaluation.and.experimentation","name":"freemium tier evaluation and experimentation","description":"Provides a free tier with meaningful monitoring capabilities that allows teams to evaluate the platform without upfront payment or credit card requirement. Enables low-risk platform assessment.","intents":["I want to try monitoring without committing budget or providing payment information","I need to evaluate if this platform meets my team's needs before purchasing","I want to start with a small pilot project at no cost"],"best_for":["startups","small teams","evaluation teams"],"limitations":["Freemium tier has limited features or usage caps","May require upgrade for production use","Limited support on free tier"],"requires":["Account creation","Basic platform access"],"input_types":["model deployments","monitoring data"],"output_types":["monitoring dashboards","alerts","reports"],"categories":["pricing","evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_2","uri":"capability://compliance.automated.compliance.audit.trail.generation","name":"automated compliance audit trail generation","description":"Automatically captures and logs all model decisions, inputs, and outputs in a compliance-ready format suitable for regulatory audits. Maintains immutable records of model behavior for regulated industries.","intents":["I need to prove to regulators what decisions my AI system made and why","I want to maintain audit trails without manual documentation","I need compliance-ready records for financial or healthcare AI applications"],"best_for":["compliance officers","enterprise teams","regulated industry practitioners"],"limitations":["Steep learning curve for non-technical compliance staff","Requires understanding of relevant regulatory frameworks","Storage costs scale with transaction volume"],"requires":["Regulatory framework specification","Model deployment integration","Data retention policies"],"input_types":["model inputs","model outputs","decision metadata"],"output_types":["audit logs","compliance reports","regulatory documentation"],"categories":["compliance","governance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_3","uri":"capability://automation.automated.response.workflow.triggering","name":"automated response workflow triggering","description":"Executes predefined automated actions when anomalies or compliance issues are detected, such as alerting teams, rolling back models, or quarantining problematic outputs. Reduces manual investigation time through intelligent automation.","intents":["I want my system to automatically respond to detected problems without waiting for human intervention","I need to reduce the time between detecting an issue and taking corrective action","I want to enforce guardrails that automatically prevent harmful outputs"],"best_for":["ML engineers","DevOps teams","production operations teams"],"limitations":["Requires careful configuration to avoid over-aggressive responses","May need manual override capabilities for edge cases","Effectiveness depends on quality of detection rules"],"requires":["Defined response policies","Integration with deployment infrastructure","Alerting system setup"],"input_types":["anomaly alerts","drift notifications","compliance violations"],"output_types":["automated actions","notifications","system state changes"],"categories":["automation","incident response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_4","uri":"capability://monitoring.model.performance.degradation.tracking","name":"model performance degradation tracking","description":"Monitors key performance metrics over time to identify gradual or sudden declines in model accuracy, latency, or other business-relevant metrics. Provides historical trends and comparative analysis.","intents":["I want to track how my model's performance changes over weeks and months","I need to identify when performance drops below acceptable thresholds","I want to correlate performance changes with data or deployment changes"],"best_for":["ML engineers","data scientists","product managers"],"limitations":["Requires ground truth labels for accuracy metrics","May lag for metrics requiring user feedback","Needs clear baseline definitions"],"requires":["Performance metric definitions","Ground truth data","Historical metric collection"],"input_types":["model predictions","ground truth labels","performance metrics"],"output_types":["performance dashboards","trend reports","degradation alerts"],"categories":["monitoring","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_5","uri":"capability://compliance.compliance.ready.dashboard.and.reporting","name":"compliance-ready dashboard and reporting","description":"Provides pre-built dashboards and automated report generation formatted for regulatory compliance requirements. Enables non-technical stakeholders to understand model behavior and compliance status.","intents":["I need to show regulators a dashboard of my AI system's compliance status","I want to generate compliance reports without manual data compilation","I need to communicate model safety metrics to non-technical executives"],"best_for":["compliance officers","executives","regulatory teams"],"limitations":["Steep learning curve for non-technical users","May require customization for specific regulatory frameworks","Limited flexibility for non-standard compliance requirements"],"requires":["Monitoring data collection","Regulatory framework specification","Dashboard access"],"input_types":["monitoring metrics","audit logs","compliance data"],"output_types":["dashboards","PDF reports","regulatory documentation"],"categories":["compliance","reporting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_6","uri":"capability://ai.safety.llm.specific.hallucination.detection","name":"llm-specific hallucination detection","description":"Specialized detection for LLM hallucinations and factual inconsistencies in generated text. Identifies when models generate plausible-sounding but false information.","intents":["I need to detect when my LLM is making up facts or information","I want to catch hallucinations before they reach end users","I need to measure hallucination rates across my LLM deployment"],"best_for":["LLM product teams","AI safety teams","customer-facing AI applications"],"limitations":["Requires domain knowledge to validate factual accuracy","May miss subtle hallucinations","Effectiveness varies by domain and task type"],"requires":["LLM inference logs","Reference knowledge bases or ground truth","Domain-specific validation rules"],"input_types":["LLM outputs","prompts","reference data"],"output_types":["hallucination flags","confidence scores","factual inconsistency reports"],"categories":["AI safety","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_7","uri":"capability://analytics.multi.model.performance.comparison.and.analysis","name":"multi-model performance comparison and analysis","description":"Compares performance metrics across multiple model versions or variants deployed in production. Enables A/B testing analysis and model selection decisions based on real production data.","intents":["I want to compare how different model versions perform in production","I need to decide whether to roll out a new model version based on performance data","I want to understand the trade-offs between different model variants"],"best_for":["ML engineers","data scientists","product teams"],"limitations":["Requires sufficient traffic to each variant for statistical significance","May need manual interpretation of results","Requires clear success metrics definition"],"requires":["Multiple model deployments","Traffic routing capability","Performance metrics collection"],"input_types":["model predictions from variants","performance metrics","user feedback"],"output_types":["comparison reports","statistical analyses","recommendation dashboards"],"categories":["analytics","experimentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_8","uri":"capability://integration.zero.setup.integration.with.existing.model.deployments","name":"zero-setup integration with existing model deployments","description":"Enables monitoring integration with existing LLM deployments without requiring model retraining, code changes, or infrastructure modifications. Minimal latency setup for rapid deployment.","intents":["I want to add monitoring to my existing LLM without rebuilding anything","I need to start monitoring production models immediately without waiting for retraining","I want to integrate monitoring without changing my current deployment architecture"],"best_for":["ML engineers","DevOps teams","production teams"],"limitations":["Limited customization for non-standard deployments","May require API key or webhook configuration","Some features may require additional instrumentation"],"requires":["API access to model serving infrastructure","Inference logs or prediction streams","Integration credentials"],"input_types":["model inference APIs","prediction logs","webhook endpoints"],"output_types":["monitoring data","alerts","dashboards"],"categories":["integration","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aporia__cap_9","uri":"capability://cost.management.token.based.usage.tracking.and.cost.monitoring","name":"token-based usage tracking and cost monitoring","description":"Tracks token consumption across LLM deployments and provides cost analysis based on token volume. Enables cost optimization and budget management for high-volume LLM applications.","intents":["I need to understand how much my LLM deployments are costing","I want to identify which applications or models are consuming the most tokens","I need to optimize costs by identifying inefficient usage patterns"],"best_for":["DevOps teams","finance teams","product managers"],"limitations":["Pricing scales aggressively with token volume","May require enterprise negotiation for high-volume applications","Limited cost optimization recommendations"],"requires":["Token consumption data","Pricing model configuration","Usage tracking"],"input_types":["token counts","model usage logs","deployment metrics"],"output_types":["cost reports","usage analytics","billing data"],"categories":["cost management","analytics"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Production LLM deployment","Historical baseline data","API integration with model serving infrastructure","Production inference pipeline","Training data statistics","Continuous data logging","Policy definitions","Content classification rules","Output filtering infrastructure","Account creation"],"failure_modes":["Requires baseline data to establish normal behavior patterns","Effectiveness depends on quality of training data","May generate false positives in early deployment phases","Requires sufficient historical data to establish baseline distributions","May not detect subtle shifts in semantic meaning","Needs regular calibration for seasonal or expected data changes","Requires clear policy definition","May generate false positives","Needs regular updates as policies evolve","Freemium tier has limited features or usage caps","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.82,"ecosystem":0.2,"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":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.550Z","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=aporia","compare_url":"https://unfragile.ai/compare?artifact=aporia"}},"signature":"1/I0xfmEXhlmCv/qdzTu/AZJ1DOLWtNldE8fxM62z0i/prd/bR8D6mc4s7h/eSpbA8BM0z+W/HVMMnKLwlw2BQ==","signedAt":"2026-06-21T22:17:58.927Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aporia","artifact":"https://unfragile.ai/aporia","verify":"https://unfragile.ai/api/v1/verify?slug=aporia","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"}}