{
  "name": "Knowledge Graph Risk Engine",
  "problem": "Risk teams need relationship-level explanations instead of opaque entity scores.",
  "domain": "knowledge-graphs",
  "architecture": "graph",
  "hugging_face_tasks": [
    "token-classification",
    "feature-extraction",
    "question-answering",
    "sentence-similarity"
  ],
  "recommended_stack": [
    "FastAPI for entity and evidence APIs",
    "Neo4j Community or PostgreSQL recursive queries",
    "Sentence Transformers for entity resolution",
    "NetworkX for local graph validation",
    "Kafka-compatible event ingestion",
    "OpenTelemetry for lineage and query traces"
  ],
  "real_world_data_sources": [
    {
      "name": "SEC EDGAR submissions API",
      "url": "https://data.sec.gov/submissions/CIK0000320193.json",
      "purpose": "Public company and filing relationships"
    },
    {
      "name": "GLEIF LEI API",
      "url": "https://api.gleif.org/api/v1/lei-records?page[size]=5",
      "purpose": "Public legal-entity identifiers and relationships"
    }
  ],
  "job_description_skills": [
    "Entity resolution and relation extraction",
    "Knowledge-graph modeling and path queries",
    "Graph-based explainability and provenance",
    "Streaming ingestion and schema evolution",
    "Risk-model evaluation and data quality controls"
  ],
  "impact_targets": [
    "Reach entity-resolution precision >= 0.95 on reviewed pairs",
    "Return evidence paths for 100% of emitted risk flags",
    "Process 10,000 relationship events per minute in load tests",
    "Detect schema and orphan-node regressions in CI"
  ],
  "baseline_evaluation": {
    "test_examples": 4,
    "accuracy": 1,
    "synthetic_evaluation": true
  },
  "estimated_delivery": "8-12 weeks for one engineer",
  "generated_baseline_is_production_ready": false
}