# Fortune 500 Case Study Validation: Amazon
# Schema: ../../contracts/case-study-validation.schema.yaml

case_study: amazon
industry: Technology/Retail
fortune_rank: 2

jtbd: "When I update an API spec, I need consistent SDK updates across 8 languages within the same commit, so that customers always have synchronized SDK versions."

scale_requirements:
  api_endpoints: 1000000
  sdk_languages: 8
  api_versions: 1000
  latency_slo: "<5min for all 8 SDKs"

required_commands:
  - command: "ggen project generate"
    purpose: "Generate multi-language SDK projects"
    criticality: P1
    current_support: pending
    gaps: []

  - command: "ggen ai generate"
    purpose: "AI-assisted SDK code generation"
    criticality: P2
    current_support: pending
    gaps: []

  - command: "ggen marketplace install"
    purpose: "Install language-specific templates"
    criticality: P2
    current_support: pending
    gaps: []

  - command: "ggen template new"
    purpose: "Create new language SDK templates"
    criticality: P3
    current_support: pending
    gaps: []

validation_status: pending
validated_date: null
validator: claude-code

test_scenarios:
  - scenario: "Multi-language SDK generation"
    steps:
      - "ggen marketplace install --package_id sdk-generator"
      - "ggen project generate --template sdk --var languages=java,python,go,rust,js,ts,ruby,php"
    expected: "All 8 SDKs generated consistently within 5 minutes"
    actual: pending

  - scenario: "API spec change propagation"
    steps:
      - "ggen template generate --template sdk.tmpl --domain api-v2.ttl"
      - "Verify all SDK files updated atomically"
    expected: "Byte-for-byte reproducible outputs across all languages"
    actual: pending

gaps: []

recommendations: []

notes: |
  Amazon case study focuses on multi-language consistency and scale.
  Key concerns: parallelism, reproducibility, atomic operations.
  Industry pattern: scale-driven with consistency requirements.
