slug: fastino-labs
provider: Fastino Labs
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: Inference
  spec_file: fastino-labs-inference-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.8
  evidence: POST /inference "Run inference (Pioneer native)"; schemas GenerateInferenceRequest, EncoderInferenceResponse,
    ReasoningConfig
  reason: Serving model inference for fine-tuned SLM/LLM models is AI/ML model operation (MLOps/model
    serving), which sits under Artificial Intelligence Management.
- tag: anthropic-compat
  spec_file: fastino-labs-anthropic-compat-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: POST /v1/messages "Messages (Anthropic-compatible)"; GET /v1/models "List available models";
    schema AnthropicMessagesRequest
  reason: An Anthropic-compatible model invocation and model catalogue surface — model serving and model
    inventory, i.e. AI/ML lifecycle operation. Tag itself is a compatibility shim, which slightly lowers
    confidence.
- tag: inference-history
  spec_file: fastino-labs-inference-history-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: GET /inferences "List inference history"; POST /inferences/{inference_id}/feedback "Submit
    inference feedback"; schema InferenceRecord
  reason: Inference record retention and feedback capture supports model monitoring and evaluation feedback
    loops, part of the AI/ML model lifecycle. Could alternatively be read as plain audit logging, hence
    moderate confidence.