Business & EnterpriseEnterprise Operations

Enterprise Document RAG & Policy Retrieval Engine

A citation-backed retrieval-augmented generation engine that indexes complex operating procedures and regulatory filings for instant query resolution.

The Operational Bottleneck

The Challenge

Operational teams lost hours searching across thousands of multi-page PDFs, technical specs, and contract files, often relying on outdated or conflicting documents.

Our Technical Execution

The Solution

Engineered an advanced hybrid search RAG pipeline utilizing parent-document chunking, dense vector indexing, and cross-encoder re-ranking to deliver factual answers with verified page citations.

System Architecture

Document ingestion microservice parses PDFs via Unstructured.io, computes embeddings with Cohere, and indexes into Qdrant. A FastAPI service handles semantic query routing with Next.js frontend UI.

Key Engineered Capabilities

  • Hybrid BM25 + dense vector semantic search across multi-format documents
  • Strict factuality guardrails preventing unverified statements
  • Direct page-level source references with interactive document viewer
  • Role-based document access controls respecting departmental permissions

Technology Stack

PythonFastAPIQdrantLangChainNext.jsDocker

Engineered Deliverables

  • Document Ingestion Pipeline
  • Semantic Search & Chat Interface
  • Role-Based Security Layer
  • Evaluation Suite

Demonstrated Real-World Impact

01

Instant query resolution with verified references replacing manual searching

02

Zero hallucinations on corporate policy lookups through strict citation constraints

03

Direct integration into internal employee communication channels

Next Steps

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