slug: refuel-ai
provider: Refuel
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: 3
edges:
- tag: Finetuned Models
  spec_file: refuel-ai-finetuned-models-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.82
  evidence: 'GET /finetuned_models/{model_id}  Get Finetuned Model From Id; PATCH ... Update Finetuned
    Model; schemas: EditModelRequest, AvailabilityStatus'
  reason: CRUD over fine-tuned LLM artefacts with availability status — ML model lifecycle management,
    matching Artificial Intelligence Management (AI/ML model lifecycle, MLOps).
- tag: Applications
  spec_file: refuel-ai-applications-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: POST /applications/{applicationName}/label  Transform rows with a deployed application; POST
    /applications/{application_id}/items/{item_id}/label  Application Feedback
  reason: Lifecycle and runtime invocation of deployed LLM labeling applications, with feedback and usage
    — AI/ML model deployment and operation (Artificial Intelligence Management). Some ambiguity as it
    also serves data enrichment/quality purposes.
- tag: Tasks
  spec_file: refuel-ai-tasks-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.72
  evidence: 'GET /tasks/{task_id}/evalset  Get Items From Evalset; GET /tasks/{task_id}/runs/{dataset_id}/metrics  Get
    Metrics For Task Run; schemas: CreateCalibrationRequest, CalibrationStatus, TaxonomyLabel'
  reason: 'Core labeling-task surface: taxonomies/labels, seedsets, evalsets, confidence calibration and
    run metrics — this is AI/ML model training-data and evaluation lifecycle management (MLOps), not generic
    work-item tracking.'