slug: nixtla
provider: Nixtla
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: 2
edges:
- tag: Finetune
  spec_file: nixtla-finetune-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.8
  evidence: POST /v2/finetune — "Foundational Time Series Model Multi Series Finetuning"
  reason: Fine-tuning a foundation model on customer data is plainly AI/ML model lifecycle work (training/MLOps),
    so Artificial Intelligence Management applies.
- tag: Finetuned Models
  spec_file: nixtla-finetuned-models-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.8
  evidence: '"List Fine-tuned Models", "Get single Fine-tuned Model", "Delete Fine-tuned Model"'
  reason: CRUD over fine-tuned model artefacts is model registry / lifecycle management, which is the
    substance of AI/ML management.