{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_orq-ai","slug":"orq-ai","name":"Orq.ai","type":"product","url":"https://orq.ai","page_url":"https://unfragile.ai/orq-ai","categories":["app-builders","deployment-infra"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_orq-ai__cap_0","uri":"capability://automation.workflow.collaborative.model.experimentation.workspace","name":"collaborative-model-experimentation-workspace","description":"Provides a shared, version-controlled environment where multiple team members can simultaneously experiment with AI models, datasets, and hyperparameters without conflicts. Uses a centralized workspace model with real-time synchronization of experiment state, allowing non-technical stakeholders to adjust model configurations through UI forms while engineers modify underlying code—all tracked in a unified audit log for governance compliance.","intents":["Enable data scientists and business analysts to collaborate on model tuning without stepping on each other's work","Allow non-technical team members to run experiments and iterate on model parameters without writing code","Maintain a complete audit trail of all model changes for regulatory compliance and reproducibility"],"best_for":["Cross-functional teams (data scientists, domain experts, business stakeholders) in regulated industries","Organizations requiring SOC 2 or HIPAA-compliant audit trails for model development"],"limitations":["Real-time collaboration may introduce latency in high-concurrency scenarios (>50 simultaneous users per workspace)","Version control is workspace-scoped; no built-in branching strategy for parallel experiment tracks","Audit log retention policies not clearly documented—unclear if logs are immutable or can be pruned"],"requires":["Team account with Orq.ai (freemium tier supports limited concurrent users)","Modern browser with WebSocket support for real-time sync","Integration with data source (S3, GCS, or local upload)"],"input_types":["CSV/Parquet datasets","Model configuration files (YAML/JSON)","Python code snippets for custom preprocessing"],"output_types":["Experiment metadata (metrics, hyperparameters, timestamps)","Audit logs (JSON format)","Model checkpoints (framework-agnostic serialization)"],"categories":["automation-workflow","collaboration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_1","uri":"capability://safety.moderation.role.based.access.control.with.model.governance","name":"role-based-access-control-with-model-governance","description":"Implements fine-grained RBAC across model development, deployment, and inference stages, with approval workflows that enforce separation of duties (e.g., data scientist trains, engineer deploys, compliance officer approves). Uses attribute-based access policies tied to model lineage, dataset provenance, and deployment environment—enabling enterprises to enforce 'no single person can push untested models to production' rules without custom code.","intents":["Enforce compliance policies that require multiple stakeholders to approve model deployments","Restrict access to sensitive datasets based on user roles and project context","Audit who accessed, modified, or deployed which models and when"],"best_for":["Regulated enterprises (financial services, healthcare, insurance) with mandatory approval workflows","Teams with strict separation-of-duties requirements (SOX, GDPR, HIPAA compliance)"],"limitations":["Policy engine appears to be UI-driven; no evidence of declarative policy-as-code (e.g., Rego, Cedar) for complex conditional logic","RBAC is platform-scoped; integrating with external identity providers (Okta, Azure AD) not clearly documented","Approval workflows are sequential; no parallel approval paths for time-sensitive deployments"],"requires":["Enterprise or higher tier subscription (freemium tier likely has limited RBAC)","Identity provider integration (SAML 2.0 or OIDC) for SSO","Defined approval workflow templates configured by admin"],"input_types":["User role definitions (JSON/YAML)","Policy rules (UI form or API payload)","Model metadata (lineage, dataset references)"],"output_types":["Access decision (allow/deny with reason)","Audit event log (JSON)","Approval workflow status (pending/approved/rejected)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_10","uri":"capability://data.processing.analysis.experiment.comparison.and.analysis","name":"experiment-comparison-and-analysis","description":"Provides side-by-side comparison of experiment results (metrics, hyperparameters, training time, resource usage) with interactive visualizations (scatter plots, parallel coordinates, heatmaps). Supports filtering experiments by tags, date range, or metric thresholds, and exporting comparison reports as PDF or CSV. Uses statistical analysis to identify which hyperparameters have the strongest correlation with model performance, helping users understand which changes matter most.","intents":["Compare multiple model experiments to identify which hyperparameters improve performance","Visualize the relationship between hyperparameters and model metrics","Export experiment comparisons for sharing with stakeholders"],"best_for":["Data scientists tuning hyperparameters who need to understand which changes matter","Teams documenting model development decisions for stakeholders"],"limitations":["Statistical analysis is limited to correlation; no causal inference or interaction effects","Visualizations are pre-built; no support for custom charts or integration with Jupyter notebooks","Comparison is limited to experiments in the same project; no cross-project comparison"],"requires":["Multiple completed experiments in the same project","Experiment metrics logged (automatically captured by Orq.ai)","Optional: custom tags for filtering experiments"],"input_types":["Experiment metadata (hyperparameters, metrics, training time)","Filter criteria (tags, date range, metric thresholds)","Export format (PDF, CSV)"],"output_types":["Comparison table (side-by-side metrics and hyperparameters)","Visualizations (scatter plots, parallel coordinates, heatmaps)","Statistical analysis (correlation matrix, feature importance)","Export file (PDF report or CSV data)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_11","uri":"capability://text.generation.language.automated.model.documentation.generation","name":"automated-model-documentation-generation","description":"Automatically generates model documentation (architecture, training data, performance metrics, limitations) from model metadata, training logs, and deployment configuration. Includes model cards (standardized documentation format), data sheets (dataset documentation), and model reports (performance analysis). Supports custom documentation templates and integrates with version control (Git) to store documentation alongside model artifacts.","intents":["Generate standardized model documentation without manual writing","Ensure all deployed models have documented limitations and performance characteristics","Store model documentation alongside artifacts for reproducibility"],"best_for":["Regulated industries requiring documented model governance and limitations","Teams wanting to standardize model documentation across projects"],"limitations":["Documentation generation is template-based; limited customization for domain-specific documentation","Git integration is one-way (documentation pushed to Git); no pull-based updates from Git","Model cards follow a standard format; no support for custom sections or domain-specific fields"],"requires":["Trained and deployed model with metadata (training data, performance metrics)","Optional: Git repository for storing documentation","Optional: custom documentation template (Markdown or HTML)"],"input_types":["Model metadata (architecture, training data, performance metrics)","Training logs (loss curves, validation metrics)","Deployment configuration (environment, resource limits)"],"output_types":["Model card (Markdown or HTML)","Data sheet (dataset documentation)","Model report (performance analysis, limitations)","Git commit (if Git integration enabled)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_2","uri":"capability://automation.workflow.end.to.end.model.lifecycle.orchestration","name":"end-to-end-model-lifecycle-orchestration","description":"Manages the complete AI model journey from data ingestion through experimentation, validation, deployment, and monitoring in a single platform using a DAG-based workflow engine. Automatically tracks lineage (which datasets fed which model versions, which models are deployed where), handles environment promotion (dev → staging → prod), and triggers retraining pipelines based on data drift or performance degradation—without requiring users to write orchestration code.","intents":["Manage model versions across multiple deployment environments without manual promotion steps","Automatically trigger retraining when production model performance drops below thresholds","Track complete data and model lineage for audit and debugging purposes"],"best_for":["Teams deploying multiple models to production who need centralized lifecycle management","Organizations with strict change management processes requiring environment promotion workflows"],"limitations":["DAG-based orchestration may not support complex conditional branching or dynamic task generation","Retraining triggers are metric-based; no support for custom event-driven triggers (e.g., 'retrain when new data arrives')","Environment promotion workflows appear to be linear (dev → staging → prod); no support for canary deployments or A/B testing infrastructure"],"requires":["Connected data source (S3, GCS, database) for automated data ingestion","Monitoring integration (Prometheus, Datadog, or custom metrics endpoint) for drift detection","Model framework support (TensorFlow, PyTorch, scikit-learn, or ONNX)"],"input_types":["Model artifacts (framework-specific or ONNX)","Dataset references (paths, SQL queries)","Performance thresholds (JSON config)","Retraining trigger rules (UI form or API)"],"output_types":["Deployment manifest (environment-specific config)","Lineage graph (JSON DAG)","Retraining job status (queued/running/completed)","Model performance metrics (time-series data)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_3","uri":"capability://automation.workflow.secure.model.deployment.with.environment.isolation","name":"secure-model-deployment-with-environment-isolation","description":"Deploys models to isolated, containerized environments with automatic secret management, network policies, and resource quotas enforced at the infrastructure level. Supports multiple deployment targets (cloud VPCs, on-premise servers, edge devices) with encrypted model artifacts and API key rotation—all managed through the UI without exposing infrastructure details to data scientists. Uses a declarative deployment manifest system that separates model logic from infrastructure configuration.","intents":["Deploy models to production without exposing infrastructure complexity to data scientists","Ensure models run in isolated environments with network and resource constraints","Rotate API keys and secrets automatically without redeploying models"],"best_for":["Enterprises requiring air-gapped or on-premise model deployments for data residency","Teams with strict security policies requiring encrypted model artifacts and secret rotation"],"limitations":["Deployment targets appear to be pre-configured by admins; unclear if users can dynamically provision new environments","Resource quotas are static; no auto-scaling based on inference load","Model artifact encryption is mentioned but key management strategy (KMS integration, HSM support) not documented"],"requires":["Deployment target configured (AWS, GCP, Azure, or self-hosted)","Container runtime (Docker or Kubernetes) on target infrastructure","Network connectivity between Orq.ai control plane and deployment targets","Secret management backend (AWS Secrets Manager, HashiCorp Vault, or Orq.ai-managed)"],"input_types":["Model artifact (ONNX, SavedModel, or framework-specific format)","Deployment manifest (YAML with resource requests, environment variables)","Secret references (API keys, database credentials)"],"output_types":["Deployment status (pending/running/failed)","Model endpoint URL (HTTP/gRPC)","Inference logs (structured JSON)","Resource utilization metrics (CPU, memory, latency)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_4","uri":"capability://data.processing.analysis.data.drift.and.model.performance.monitoring","name":"data-drift-and-model-performance-monitoring","description":"Continuously monitors production model inputs and outputs against baseline distributions, automatically detecting data drift (e.g., feature distributions shift beyond thresholds) and performance degradation (accuracy, latency, business metrics drop). Integrates with external monitoring systems (Prometheus, Datadog) or uses built-in metrics collection via model inference logs. Triggers alerts and optional automated retraining pipelines when anomalies are detected, with configurable thresholds and notification channels.","intents":["Detect when production data distribution changes and models may need retraining","Monitor model inference latency and accuracy in real-time without manual log analysis","Automatically trigger retraining or rollback when model performance degrades"],"best_for":["Teams deploying models to production who need early warning of performance issues","Regulated industries requiring continuous model monitoring for compliance audits"],"limitations":["Drift detection appears to be statistical (e.g., Kolmogorov-Smirnov test); no support for semantic drift (e.g., 'user behavior changed') without custom metrics","Baseline distributions must be manually configured; no automatic baseline learning from training data","Alert routing is limited to email/Slack; no native integration with PagerDuty or custom webhooks"],"requires":["Production model deployment with inference logging enabled","Baseline dataset or reference distribution (from training or validation set)","Monitoring backend (Prometheus, Datadog) or Orq.ai-managed metrics storage","Alert notification channel (email, Slack, or webhook)"],"input_types":["Inference logs (JSON with features, predictions, timestamps)","Baseline distribution (statistical summary or raw data)","Drift threshold configuration (JSON with metric names and thresholds)","Performance metric definitions (custom SQL or predefined metrics)"],"output_types":["Drift alert (JSON with detected shift, magnitude, timestamp)","Performance report (time-series metrics with anomaly flags)","Retraining trigger event (if configured)","Monitoring dashboard (UI with charts and alerts)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_5","uri":"capability://automation.workflow.model.versioning.and.rollback.management","name":"model-versioning-and-rollback-management","description":"Maintains a complete version history of all model artifacts, configurations, and deployment states with the ability to instantly rollback to any previous version. Uses immutable model snapshots tagged with metadata (training date, dataset version, performance metrics, approver) and supports comparing metrics across versions to identify regressions. Integrates with deployment workflows to enable one-click rollback if a production model fails, with automatic traffic rerouting to the previous stable version.","intents":["Quickly rollback to a previous model version if a production deployment causes issues","Compare model performance across versions to identify which changes caused regressions","Maintain a complete audit trail of model changes for compliance and debugging"],"best_for":["Teams deploying models frequently who need fast rollback capabilities","Regulated industries requiring immutable model artifact storage and audit trails"],"limitations":["Rollback is model-level; no support for partial rollbacks (e.g., 'use model v2 for segment A, v1 for segment B')","Version comparison is UI-driven; no API for programmatic version diffing","Model artifact storage is platform-managed; no option to use external model registries (e.g., Hugging Face Model Hub)"],"requires":["Model artifact in supported format (ONNX, SavedModel, scikit-learn pickle, or framework-specific)","Metadata tags (training date, dataset version, performance metrics) provided at model registration","Deployment integration enabled for one-click rollback"],"input_types":["Model artifact (binary or serialized format)","Model metadata (JSON with tags, metrics, approver)","Deployment state (current version, traffic split)"],"output_types":["Version history (list with metadata and timestamps)","Version comparison report (metrics diff, config diff)","Rollback confirmation (previous version deployed, traffic rerouted)","Audit log entry (who rolled back, when, why)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_6","uri":"capability://text.generation.language.non.technical.model.configuration.ui","name":"non-technical-model-configuration-ui","description":"Provides a form-based UI for configuring model hyperparameters, data preprocessing steps, and training settings without writing code. Uses schema-driven form generation that adapts based on selected model type (e.g., showing 'learning rate' for neural networks but 'max depth' for decision trees). Includes built-in validation, tooltips with explanations, and preset configurations for common use cases—enabling business analysts and domain experts to run experiments without data science expertise.","intents":["Allow non-technical stakeholders to adjust model parameters and run experiments without writing code","Reduce friction for domain experts to contribute to model development","Standardize model configurations across teams using preset templates"],"best_for":["Cross-functional teams with non-technical domain experts (business analysts, product managers)","Organizations wanting to democratize AI development beyond data scientists"],"limitations":["Form-based UI is limited to predefined parameters; custom preprocessing logic requires code","Preset configurations are curated by Orq.ai; no user-defined templates or community-shared configs","Form validation is client-side; no server-side validation of parameter combinations (e.g., 'batch size > dataset size')"],"requires":["Modern browser with JavaScript support","Basic understanding of model concepts (no coding required, but domain knowledge helpful)","Access to training dataset (uploaded or connected via data source)"],"input_types":["Model type selection (dropdown)","Hyperparameter values (text inputs, sliders, dropdowns)","Data preprocessing options (checkboxes, multi-select)","Training settings (epochs, batch size, validation split)"],"output_types":["Model configuration (JSON or YAML)","Training job submission (queued for execution)","Experiment results (metrics, visualizations)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_7","uri":"capability://data.processing.analysis.dataset.versioning.and.lineage.tracking","name":"dataset-versioning-and-lineage-tracking","description":"Tracks all dataset versions used in model training with automatic lineage graphs showing which datasets fed which model versions and how data was transformed. Supports dataset snapshots (immutable copies at specific points in time), data profiling (schema, statistics, sample rows), and data validation rules that flag quality issues before training. Integrates with data sources (S3, GCS, databases) to detect when upstream data changes and automatically flag affected models.","intents":["Understand which datasets were used to train which models for reproducibility and debugging","Detect when upstream data changes and identify which models may be affected","Validate data quality before training to catch issues early"],"best_for":["Teams with complex data pipelines who need to track data lineage for compliance","Organizations requiring reproducible model training with documented data provenance"],"limitations":["Data profiling is basic (schema, statistics); no advanced data quality checks (e.g., PII detection, outlier analysis)","Lineage tracking is limited to datasets and models; no support for tracking intermediate transformations or feature engineering steps","Data validation rules are UI-driven; no support for custom validation logic or integration with data quality frameworks (Great Expectations)"],"requires":["Connected data source (S3, GCS, database, or local upload)","Dataset in tabular format (CSV, Parquet, SQL query result)","Optional: data validation rules (UI form or JSON schema)"],"input_types":["Dataset (CSV, Parquet, or SQL query)","Data validation rules (JSON schema or UI form)","Transformation metadata (if applicable)"],"output_types":["Dataset version (immutable snapshot with metadata)","Data profile (schema, statistics, sample rows)","Lineage graph (JSON DAG showing dataset → model relationships)","Data quality report (validation results, flags)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_8","uri":"capability://tool.use.integration.inference.api.generation.and.management","name":"inference-api-generation-and-management","description":"Automatically generates REST or gRPC inference APIs from deployed models with built-in request validation, rate limiting, and authentication (API keys, OAuth). Supports batch inference (submit multiple samples, get results asynchronously) and real-time inference (single sample, immediate response). Includes API documentation (OpenAPI/Swagger), client SDK generation (Python, JavaScript), and usage analytics (requests/second, latency percentiles, error rates).","intents":["Expose deployed models as production-ready APIs without writing server code","Generate client libraries for consuming model APIs in different languages","Monitor API usage and performance in real-time"],"best_for":["Teams deploying models to production who need to expose them via APIs","Organizations requiring API documentation and client SDKs for model consumption"],"limitations":["API generation is limited to standard REST/gRPC; no support for custom protocols or streaming responses","Rate limiting is global; no per-user or per-endpoint rate limits","Client SDK generation is limited to Python and JavaScript; no support for Java, Go, or other languages"],"requires":["Deployed model in Orq.ai","API authentication method (API key or OAuth provider)","Optional: custom request/response schema (JSON schema)"],"input_types":["Model artifact (deployed in Orq.ai)","API configuration (authentication method, rate limits, batch size)","Custom schema (optional, for request/response validation)"],"output_types":["REST/gRPC API endpoint (URL)","OpenAPI/Swagger documentation (JSON)","Client SDK (Python, JavaScript packages)","Usage analytics (JSON with metrics)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_orq-ai__cap_9","uri":"capability://automation.workflow.team.and.project.organization.with.permissions","name":"team-and-project-organization-with-permissions","description":"Organizes models, datasets, and experiments into projects with team-level access control. Supports nested teams (e.g., 'Data Science > NLP Team'), role-based permissions (viewer, editor, admin), and resource sharing across projects. Uses a hierarchical permission model where parent team permissions cascade to child resources, with the ability to override at the project level. Includes team invitations, member management, and activity logs showing who accessed what resources.","intents":["Organize AI development work into logical projects with team-level access control","Share models and datasets across teams without exposing sensitive resources","Track who accessed which resources for audit and security purposes"],"best_for":["Large organizations with multiple teams working on different AI projects","Teams requiring fine-grained access control and resource sharing policies"],"limitations":["Permission model is hierarchical; no support for matrix organizations (e.g., 'belongs to both Data Science and Product teams')","Resource sharing is project-scoped; no cross-project sharing without creating shared projects","Activity logs are UI-only; no API for programmatic access to audit logs"],"requires":["Orq.ai account with team management enabled","Team members with email addresses for invitations","Optional: SSO integration (SAML 2.0 or OIDC) for centralized identity management"],"input_types":["Team structure (nested teams, member list)","Role assignments (viewer, editor, admin per team/project)","Resource sharing policies (which teams can access which projects)"],"output_types":["Team hierarchy (JSON tree)","Permission matrix (who has what access to which resources)","Activity log (JSON with user, action, resource, timestamp)","Team invitation link (shareable URL)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Team account with Orq.ai (freemium tier supports limited concurrent users)","Modern browser with WebSocket support for real-time sync","Integration with data source (S3, GCS, or local upload)","Enterprise or higher tier subscription (freemium tier likely has limited RBAC)","Identity provider integration (SAML 2.0 or OIDC) for SSO","Defined approval workflow templates configured by admin","Multiple completed experiments in the same project","Experiment metrics logged (automatically captured by Orq.ai)","Optional: custom tags for filtering experiments","Trained and deployed model with metadata (training data, performance metrics)"],"failure_modes":["Real-time collaboration may introduce latency in high-concurrency scenarios (>50 simultaneous users per workspace)","Version control is workspace-scoped; no built-in branching strategy for parallel experiment tracks","Audit log retention policies not clearly documented—unclear if logs are immutable or can be pruned","Policy engine appears to be UI-driven; no evidence of declarative policy-as-code (e.g., Rego, Cedar) for complex conditional logic","RBAC is platform-scoped; integrating with external identity providers (Okta, Azure AD) not clearly documented","Approval workflows are sequential; no parallel approval paths for time-sensitive deployments","Statistical analysis is limited to correlation; no causal inference or interaction effects","Visualizations are pre-built; no support for custom charts or integration with Jupyter notebooks","Comparison is limited to experiments in the same project; no cross-project comparison","Documentation generation is template-based; limited customization for domain-specific documentation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"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":"2026-05-24T12:16:32.436Z","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=orq-ai","compare_url":"https://unfragile.ai/compare?artifact=orq-ai"}},"signature":"J77MmnCLyPMjXLnq4ykZdMAzsadwRWbihI9c0zHPZdKnY9UAgMwxNIGI+YqfmkeGcf52lQ1vX+QpblxlK5bDAQ==","signedAt":"2026-06-22T07:56:46.922Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/orq-ai","artifact":"https://unfragile.ai/orq-ai","verify":"https://unfragile.ai/api/v1/verify?slug=orq-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"}}