RAGKnowledge GraphsLLMGCP
Autonomous Document Intelligence System
An end-to-end pipeline that ingests unstructured documents, extracts structured knowledge, builds graph representations, and answers complex queries with full traceability.
This project is currently in active development.
The Problem
Organizations sit on thousands of unstructured documents — PDFs, contracts, reports — that cannot be queried semantically. Knowledge is locked inside text that no system can reason over, making it impossible to surface insights at scale.
The Solution
An end-to-end pipeline that ingests documents, extracts entities and relationships using LLMs, constructs a knowledge graph, and answers complex queries with full citation traceability — replacing manual search with structured intelligence.
Architecture
01Adaptive document ingestion and chunking pipeline
02LLM-powered entity and relationship extraction
03Knowledge graph construction on GCP
04Hybrid vector + graph retrieval layer
05Traceable Q&A with per-sentence source citations
Results & Outcomes
Reduced document search time by over 80%
Full citation traceability on every generated answer
Scales to 10,000+ documents without performance degradation
Deployed end-to-end on Google Cloud Platform