slug: nanonets
provider: Nanonets
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: OCR Train
  spec_file: nanonets-ocr-train-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.78
  evidence: POST /OCR/Model/{model_id}/Train/ — 'Train Model'; 'Upload Training Images By File'
  reason: Operations upload training data and train/retrain a named model — plainly ML model lifecycle
    (training, retraining) which maps to Artificial Intelligence Management. Not a document-business capability;
    it is model ops.
- tag: OCR Predict
  spec_file: nanonets-ocr-predict-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.72
  evidence: '''Synchronous and asynchronous OCR prediction and training endpoints for Nanonets custom
    OCR models ... train or retrain a model.'''
  reason: Beyond inference, the surface explicitly covers training/retraining of custom OCR models, which
    is AI/ML model lifecycle management under Information & Data Management.
- tag: Image Classification Predict
  spec_file: nanonets-image-classification-predict-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: '''Predict against a Nanonets image classification model. Submit images either as multipart
    file uploads or as a batch of publicly accessible URLs.'''
  reason: Operations serve ML model inference ('Prediction For Image File'), which falls under Artificial
    Intelligence Management (AI/ML model lifecycle). Confidence moderate since inference-only endpoints
    are arguably just runtime usage rather than model lifecycle management.