{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_citrusx","slug":"citrusx","name":"CitrusX","type":"product","url":"https://www.citrusx.ai","page_url":"https://unfragile.ai/citrusx","categories":["observability"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_citrusx__cap_0","uri":"capability://productivity.real.time.model.performance.monitoring","name":"real-time model performance monitoring","description":"Continuously tracks machine learning model behavior in production, detecting performance degradation, data drift, and prediction quality changes as they occur. Provides automated alerts when models deviate from expected performance baselines.","intents":["I need to know immediately when my model starts performing worse in production","I want to catch data drift before it impacts business decisions","I need visibility into whether my model is still making good predictions"],"best_for":["ML engineers","data scientists","MLOps teams","enterprises with production models"],"limitations":["Requires models already deployed in production","Needs historical baseline data for comparison","Effectiveness depends on quality of training data"],"requires":["Production ML model","Model prediction logs","Historical performance data","Integration with model serving infrastructure"],"input_types":["model predictions","actual outcomes","feature data"],"output_types":["performance metrics","alerts","dashboard visualizations"],"categories":["productivity","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_1","uri":"capability://productivity.automated.bias.detection.across.demographics","name":"automated bias detection across demographics","description":"Analyzes model predictions to identify disparate impact and fairness violations across protected demographic groups without requiring manual configuration. Detects systematic differences in model behavior across gender, race, age, and other demographic dimensions.","intents":["I need to ensure my model doesn't discriminate against protected groups","I want to identify if my model treats different demographics unfairly","I need to measure and report on model fairness metrics for compliance"],"best_for":["compliance officers","risk managers","enterprises in regulated industries","financial institutions","hiring platforms"],"limitations":["Requires demographic data in dataset","Cannot detect bias in unmeasured dimensions","Fairness metrics are context-dependent and may need customization"],"requires":["Model predictions","Demographic attributes in data","Ground truth labels","Definition of fairness metrics relevant to use case"],"input_types":["model predictions","demographic data","actual outcomes"],"output_types":["fairness metrics","bias reports","demographic parity analysis"],"categories":["productivity","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_10","uri":"capability://productivity.integration.with.ml.model.serving.platforms","name":"integration with ml model serving platforms","description":"Connects with production ML infrastructure including model serving frameworks, prediction APIs, and data pipelines. Enables seamless monitoring without requiring code changes to existing systems.","intents":["I want to monitor my models without rebuilding my ML infrastructure","I need to integrate fairness monitoring into my existing MLOps stack","I want to add monitoring to models already in production"],"best_for":["ML engineers","MLOps teams","enterprises with existing ML infrastructure"],"limitations":["Integration complexity varies by platform","May require API access to model serving systems","Some legacy systems may not be supported"],"requires":["Access to model serving infrastructure","API credentials","Network connectivity","Supported platform"],"input_types":["API connections","prediction streams","model metadata"],"output_types":["integrated monitoring","unified dashboards"],"categories":["productivity","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_11","uri":"capability://productivity.fairness.constraint.enforcement.and.guardrails","name":"fairness constraint enforcement and guardrails","description":"Enables definition and enforcement of fairness constraints that models must satisfy. Can block or flag predictions that violate defined fairness guardrails before they reach users.","intents":["I want to prevent my model from making unfair decisions","I need to enforce fairness constraints in production","I want to flag predictions that violate fairness rules before they're used"],"best_for":["compliance teams","risk managers","model governance teams"],"limitations":["Enforcing constraints may reduce model accuracy","Requires clear definition of acceptable fairness tradeoffs","May impact system latency"],"requires":["Defined fairness constraints","Real-time prediction processing","Decision-making authority","Fallback mechanisms"],"input_types":["predictions","fairness constraints","demographic data"],"output_types":["approved predictions","flagged predictions","constraint violations"],"categories":["productivity","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_2","uri":"capability://productivity.decision.drift.and.fairness.violation.alerting","name":"decision drift and fairness violation alerting","description":"Automatically detects when model decisions begin to diverge from expected patterns or when fairness metrics cross defined thresholds. Generates real-time alerts to flag potential issues before they escalate.","intents":["I want to be alerted the moment my model's fairness metrics degrade","I need to catch decision drift before it causes business harm","I want automated notifications when models violate fairness constraints"],"best_for":["compliance teams","risk management","model governance teams","regulated enterprises"],"limitations":["Requires pre-defined thresholds and baselines","Alert fatigue possible if thresholds not properly calibrated","Cannot prevent violations, only detect them"],"requires":["Established fairness baselines","Defined alert thresholds","Real-time prediction stream","Notification infrastructure"],"input_types":["model predictions","fairness metrics","demographic data"],"output_types":["alerts","notifications","incident reports"],"categories":["productivity","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_3","uri":"capability://productivity.model.explainability.and.decision.interpretation","name":"model explainability and decision interpretation","description":"Provides interpretable explanations for individual model predictions and aggregate model behavior patterns. Helps stakeholders understand why models make specific decisions and what factors drive predictions.","intents":["I need to explain to regulators why my model made a specific decision","I want to understand which features are most important in my model's predictions","I need to provide customers with explanations for decisions that affect them"],"best_for":["compliance officers","customer-facing teams","regulators","model developers","risk managers"],"limitations":["Explainability quality varies by model type","Some complex models are inherently harder to explain","Explanations may not capture all decision factors"],"requires":["Trained model","Feature data","Prediction logs","Model architecture information"],"input_types":["model predictions","feature values","model weights"],"output_types":["feature importance scores","decision explanations","SHAP values","interpretation reports"],"categories":["productivity","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_4","uri":"capability://productivity.regulatory.compliance.reporting.and.audit.trails","name":"regulatory compliance reporting and audit trails","description":"Generates comprehensive audit logs and compliance reports documenting model behavior, fairness metrics, and decision-making processes. Supports evidence collection for regulatory requirements like EU AI Act and FCRA.","intents":["I need to demonstrate to regulators that my AI system is fair and transparent","I want to create audit trails proving my model meets compliance requirements","I need to generate reports for regulatory submissions and audits"],"best_for":["compliance officers","legal teams","regulated enterprises","financial institutions","healthcare organizations"],"limitations":["Reports are only as good as underlying monitoring data","Compliance requirements vary by jurisdiction","Requires understanding of applicable regulations"],"requires":["Complete monitoring data","Fairness metrics","Decision logs","Model documentation","Regulatory framework definitions"],"input_types":["monitoring data","fairness metrics","decision logs","model metadata"],"output_types":["compliance reports","audit logs","regulatory documentation","evidence packages"],"categories":["productivity","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_5","uri":"capability://productivity.model.behavior.dashboard.and.visualization","name":"model behavior dashboard and visualization","description":"Provides an interactive dashboard displaying real-time model performance, fairness metrics, and decision patterns across demographic groups. Enables quick visual identification of issues and trends.","intents":["I want a single view of all my model's health and fairness metrics","I need to quickly spot problems in model behavior without digging through logs","I want to show stakeholders visual evidence of model fairness"],"best_for":["ML engineers","data scientists","managers","compliance teams","executives"],"limitations":["Dashboard effectiveness depends on metric selection","Real-time updates may have latency","Requires training to interpret complex visualizations"],"requires":["Real-time monitoring data","Fairness metrics","Performance metrics","Web infrastructure"],"input_types":["monitoring data","metrics","logs"],"output_types":["interactive visualizations","charts","dashboards","reports"],"categories":["productivity","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_6","uri":"capability://productivity.multi.model.fairness.comparison.and.benchmarking","name":"multi-model fairness comparison and benchmarking","description":"Compares fairness and performance metrics across multiple models to identify which models best balance accuracy with fairness. Enables data-driven model selection based on fairness criteria.","intents":["I want to compare fairness metrics across different model versions","I need to choose between models based on both accuracy and fairness","I want to benchmark my model's fairness against industry standards"],"best_for":["data scientists","ML engineers","model governance teams"],"limitations":["Requires multiple models to compare","Fairness-accuracy tradeoffs are context-dependent","Benchmarks may not be available for all domains"],"requires":["Multiple trained models","Predictions from each model","Fairness metrics for each model","Performance baselines"],"input_types":["model predictions","fairness metrics","performance metrics"],"output_types":["comparison reports","fairness rankings","tradeoff analysis"],"categories":["productivity","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_7","uri":"capability://productivity.demographic.parity.and.disparate.impact.analysis","name":"demographic parity and disparate impact analysis","description":"Calculates statistical measures of fairness including demographic parity, disparate impact ratios, and equal opportunity metrics. Quantifies whether model outcomes differ significantly across demographic groups.","intents":["I need to calculate disparate impact ratios for regulatory compliance","I want to measure if my model achieves demographic parity","I need statistical evidence of fairness or unfairness in my model"],"best_for":["compliance officers","data scientists","legal teams","risk managers"],"limitations":["Requires demographic labels in data","Different fairness metrics may conflict","Statistical significance depends on sample size"],"requires":["Model predictions","Demographic attributes","Actual outcomes","Sample size sufficient for statistical analysis"],"input_types":["predictions","demographic data","outcomes"],"output_types":["fairness metrics","statistical tests","disparate impact ratios","parity reports"],"categories":["productivity","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_8","uri":"capability://productivity.model.prediction.logging.and.versioning","name":"model prediction logging and versioning","description":"Captures and stores all model predictions with associated metadata, enabling historical analysis and audit trails. Maintains version history of models and their predictions for compliance and debugging.","intents":["I need to keep a complete record of every prediction my model made","I want to audit what my model predicted at any point in time","I need to trace back decisions to specific model versions"],"best_for":["compliance teams","ML engineers","auditors","regulated enterprises"],"limitations":["Storage costs scale with prediction volume","Requires sufficient infrastructure for logging","Privacy concerns with storing sensitive prediction data"],"requires":["Model serving infrastructure","Storage system","Logging pipeline","Data retention policies"],"input_types":["model predictions","metadata","timestamps"],"output_types":["prediction logs","version history","audit trails"],"categories":["productivity","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_citrusx__cap_9","uri":"capability://productivity.custom.fairness.metric.definition.and.tracking","name":"custom fairness metric definition and tracking","description":"Allows teams to define custom fairness metrics tailored to their specific business context and regulatory requirements. Tracks these metrics over time and alerts when they deviate from targets.","intents":["I need to track fairness metrics specific to my industry or use case","I want to define fairness thresholds that match my business requirements","I need to monitor custom fairness definitions alongside standard metrics"],"best_for":["data scientists","compliance officers","model governance teams"],"limitations":["Requires domain expertise to define meaningful metrics","Custom metrics may not be comparable across organizations","Poorly defined metrics can be misleading"],"requires":["Understanding of fairness concepts","Domain expertise","Data for metric calculation","Metric definition interface"],"input_types":["metric definitions","predictions","demographic data"],"output_types":["custom metrics","metric reports","alerts"],"categories":["productivity","compliance"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Production ML model","Model prediction logs","Historical performance data","Integration with model serving infrastructure","Model predictions","Demographic attributes in data","Ground truth labels","Definition of fairness metrics relevant to use case","Access to model serving infrastructure","API credentials"],"failure_modes":["Requires models already deployed in production","Needs historical baseline data for comparison","Effectiveness depends on quality of training data","Requires demographic data in dataset","Cannot detect bias in unmeasured dimensions","Fairness metrics are context-dependent and may need customization","Integration complexity varies by platform","May require API access to model serving systems","Some legacy systems may not be supported","Enforcing constraints may reduce model accuracy","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.43333333333333335,"quality":0.86,"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":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.537Z","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=citrusx","compare_url":"https://unfragile.ai/compare?artifact=citrusx"}},"signature":"fAmVrzoLRhRJ+28SqpEziOHT0pbS6ipXqm4hVmwwtOcT5eXHNj8lBuI5p4u1FMPFi5xIWgwHW+v6lCLfUE/FCA==","signedAt":"2026-06-21T14:36:59.793Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/citrusx","artifact":"https://unfragile.ai/citrusx","verify":"https://unfragile.ai/api/v1/verify?slug=citrusx","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"}}