{
  "version": "0.1.0",
  "generatedAt": "2026-05-16T00:00:00+05:30",
  "canonicalPath": "/case-studies/enterprise-rag",
  "markdownPath": "/reports/case-study-packets/enterprise-rag.md",
  "jsonPath": "/reports/case-study-packets/enterprise-rag.json",
  "slug": "enterprise-rag",
  "title": "Enterprise RAG System",
  "client": "Large institution",
  "summary": "A retrieval-augmented generation system for searching institutional documents, chunking knowledge sources, and answering staff queries through a focused interface.",
  "outcome": "Created a practical path from unstructured documents to reliable internal knowledge access.",
  "image": "/case-studies/event5.png",
  "challenge": "The institution had unstructured documents and needed a practical internal search surface that returned grounded answers rather than confident unsupported summaries.",
  "stats": [
    "Document chunking",
    "Semantic search",
    "Citation-first answers",
    "Custom UI"
  ],
  "stack": [
    "Vector database",
    "FastAPI",
    "LangChain/LlamaIndex",
    "React frontend"
  ],
  "approach": [
    "Designed the retrieval pipeline around document chunking, metadata, and citation display.",
    "Built a focused staff-facing UI instead of exposing raw embedding/search internals.",
    "Prioritized answer provenance so users could inspect the source document behind each claim.",
    "Outlined a production path for permissions, refresh cadence, and evaluation of retrieval misses."
  ],
  "evidence": [
    [
      "Knowledge shape",
      "Unstructured documents"
    ],
    [
      "Answer mode",
      "Citation-first"
    ],
    [
      "Interface",
      "Custom staff UI"
    ],
    [
      "Primary risk controlled",
      "Unsupported answers"
    ]
  ],
  "timeline": [
    "Document audit",
    "Chunking strategy",
    "Vector search",
    "Answer UI",
    "Grounding review"
  ],
  "risks": [
    "Stale documents",
    "Permission leakage",
    "Bad chunk boundaries",
    "Citation mismatch"
  ],
  "nextEvidenceStep": "Replace provisional metrics with client-approved screenshots, raw artifacts, and final numbers when available."
}