slug: superb-ai
provider: Superb AI
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Software & Technology
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 10
edges:
- tag: Training
  spec_file: superb-ai-training-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.92
  evidence: training-create_training_run 'Create Training Run', training-get_training_metrics, schemas
    TrainingRunStatus, SplitStrategy, CatalogEarlyStoppingKnob
  reason: Launching, monitoring and cancelling model training runs with hyperparameter catalogue and metrics
    is the core of ML model lifecycle / MLOps.
- tag: Models
  spec_file: superb-ai-models-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.88
  evidence: GET /tenants/{slug}/models 'List Models', schemas TrainedModelResponse, TrainingMetricsSummary,
    ConfusionMatrix
  reason: Operations manage the registry of trained ML models and their evaluation metrics — squarely
    AI/ML model lifecycle (MLOps) within Information & Data Management.
- tag: Deployments
  spec_file: superb-ai-deployments-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.85
  evidence: POST /tenants/{slug}/models/{model_id}/deployments  'Deploy Model'; POST /tenants/{slug}/deployments/{deployment_id}/predict  'Predict'
  reason: Deploying trained models and serving predictions is model-serving/MLOps, squarely Artificial
    Intelligence Management rather than general software deployment orchestration.
- tag: Foundation Models
  spec_file: superb-ai-foundation-models-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.85
  evidence: GET /tenants/{slug}/foundation-models 'List Foundation Models'; POST /tenants/{slug}/foundation-models/{key}/predict
    'Predict'; ZeroPredictRequest, FoundationModelStatus
  reason: Catalogue and zero-shot inference against foundation models is AI/ML model lifecycle management.
- tag: auto-label
  spec_file: superb-ai-auto-label-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.85
  evidence: auto-label-create_run 'Create Run', auto-label-suggest 'Suggest', schemas AutoLabelSuggestion,
    ModelRefKind, AutoLabelConfig
  reason: Model-driven automated annotation runs and inference suggestions — applied ML inference within
    the data-centric AI lifecycle.
- tag: Tenants
  spec_file: superb-ai-tenants-api-openapi.yml
  capability_id: BC-4230.10
  capability_id_l1: BC-4230
  capability_name: Tenant Provisioning & Lifecycle
  confidence: 0.75
  evidence: POST /tenants tenants-create_tenant 'Create Tenant'; description 'Multi-tenant labeling-platform
    backend'; schemas TenantCreate, OwnerCreate
  reason: Creation of tenants with an owner in an explicitly multi-tenant SaaS backend is tenant provisioning/lifecycle;
    surface is small so confidence is moderate.
- tag: Annotations
  spec_file: superb-ai-annotations-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: POST /tenants/{slug}/projects/{project_id}/annotations/batch-create — 'Project Batch Create
    Annotations'; schemas BboxGeometry, KeypointGeometryRead, AnnotationSource
  reason: Creation and revision of image annotations (bounding boxes, polygons, keypoints) is training-data
    labelling within the ML/MLOps lifecycle, which the frame places under Artificial Intelligence Management.
- tag: Segment
  spec_file: superb-ai-segment-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: POST /tenants/{slug}/projects/{project_id}/assets/{asset_id}/segment, schemas SegmentSuggestion,
    PolygonGeometryRead
  reason: Model-assisted segmentation inference producing annotation geometry suggestions — part of the
    AI/ML annotation pipeline.
- tag: image_assets
  spec_file: superb-ai-image-assets-api-openapi.yml
  capability_id: BC-610
  capability_id_l1: BC-610
  capability_name: Information & Data Management
  confidence: 0.7
  evidence: image_assets-upload_init 'Upload Init', image_assets-search_assets 'Search Assets', schemas
    AssetResponse, EmbeddingStatus, SearchHit
  reason: Ingestion, cataloguing, search and bulk deletion of image data assets used for ML — information
    and data asset management; no single L2 clearly dominates (data management vs AI lifecycle).
- tag: image_datasets
  spec_file: superb-ai-image-datasets-api-openapi.yml
  capability_id: BC-610
  capability_id_l1: BC-610
  capability_name: Information & Data Management
  confidence: 0.7
  evidence: image_datasets-create_image_dataset 'Create Image Dataset', image_datasets-embed_image_dataset
    'Embed Image Dataset', DatasetStatsResponse
  reason: Lifecycle and statistics of image datasets plus embedding generation — management of data assets
    feeding ML; L1 data management is defensible, the precise L2 is ambiguous.