AI Retrieval That Understands Relationships — Not Just Keywords
Standard RAG retrieves document chunks based on semantic similarity. It fails when answers require connecting information across multiple documents. GraphRAG adds a knowledge graph layer that maps entities and relationships, enabling AI to answer complex multi-hop questions that standard RAG cannot handle.
Standard RAG Fails on Questions That Require Reasoning
RAG works by converting documents into vector embeddings and retrieving semantically similar chunks. This handles direct questions well. But many business-critical questions require reasoning across documents: 'Which vendors have regulatory violations?' requires connecting vendor records, regulatory databases, and news sources.
Standard RAG retrieves chunks in isolation. It has no concept of entity relationships, no way to traverse connections, and no ability to aggregate from multiple sources. For complex analytical queries, standard RAG gives incomplete answers 40-60% of the time.
Knowledge Graphs + Vector Retrieval for Complete Answers
GraphRAG combines vector search for semantic similarity and graph traversal for relationship reasoning.
During indexing, we extract entities (people, companies, products, regulations) and relationships into a knowledge graph linked to source documents.
During retrieval, vector search finds relevant chunks while graph traversal discovers related entities. The combined context enables accurate multi-hop answers.
The knowledge graph also enables entity disambiguation, temporal reasoning, and aggregation queries. Community detection identifies entity clusters for global summarization.
GraphRAG Development in 4 Phases
Data & Query Analysis(1-2 weeks)
Analyze document corpus, identify entity and relationship types, catalog complex queries your team needs answered.
Knowledge Graph Design(1-2 weeks)
Design graph schema: entity types, relationships, extraction rules, disambiguation logic.
Build & Index(3-5 weeks)
Build extraction pipeline, construct knowledge graph, integrate with vector search, implement hybrid retrieval.
Deploy & Optimize(2 weeks + ongoing)
Deploy connected to AI applications. Monitor accuracy and graph completeness. Extraction pipeline runs on new documents automatically.
GraphRAG Technology Stack
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GraphRAG Use Cases
Pharmaceutical
Challenge: Drug interaction research required cross-referencing 10,000+ papers — months of work per analysis
Solution: GraphRAG with drug, protein, gene, disease entities. Researchers query naturally and get answers spanning thousands of documents
Result: Research time reduced from weeks to minutes; previously unknown interaction patterns discovered
Legal
Challenge: M&A due diligence required reviewing thousands of contracts to identify risks — 3-4 week process
Solution: GraphRAG extracting parties, obligations, termination clauses from deal documents with cross-contract analysis
Result: Review reduced from 4 weeks to 1 week; identified 23% more risk factors than manual review
Intelligence
Challenge: Analysts needed to connect information across thousands of reports to identify threat patterns
Solution: GraphRAG mapping people, organizations, locations, events with multi-hop queries revealing indirect connections
Result: Connection discovery rate improved 5x; analyst report production decreased 40%
Enterprise Knowledge
Challenge: 50,000+ document knowledge base failed on complex queries like 'Who worked on similar projects?'
Solution: GraphRAG mapping people, projects, technologies, decisions. Complex queries successful across all documentation
Result: Complex queries successful 78% vs. 25% with standard search; engineer onboarding time reduced 30%
Why idataweb for GraphRAG
Modern Production Stack
Data systems built on Next.js 16 + PostgreSQL with pgvector for embeddings and similarity search. No external vector database fees. Payload CMS 3 manages data sources and pipeline configuration through an admin panel your team controls directly.
AI-Native Team
We use Claude, GPT-4o, Deepgram, and ElevenLabs in production daily — for coding, content generation, voice automation, and customer interactions. We're not consultants who read about AI; we're practitioners who ship AI systems every week.
Self-Hosted Infrastructure
Your data stays on your infrastructure. PostgreSQL with pgvector handles embeddings locally — no external vector database sending your proprietary information to third-party servers. Self-hosted means GDPR-compliant by architecture.
End-to-End Delivery
Strategy, architecture, development, deployment, and ongoing support — all from one team. No handoffs between consultants, designers, and developers. The engineers who build your system are the same ones who maintain it.
Automation-First Operations
Our own operations are automated end-to-end: CI/CD pipelines, infrastructure monitoring with Telegram alerts, daily database backups, automated content publishing, and AI-assisted development workflows. We build automation for clients because automation is how we run our own business.
Transparent Fixed Pricing
Fixed-price projects with clear milestones and deliverables. You approve each phase before we proceed to the next. No open-ended hourly billing, no scope creep surprises. Ongoing support is a separate, transparent monthly agreement.
Frequently Asked Questions
How much does GraphRAG cost?
Focused system (1,000-10,000 docs) starts at $25,000-$45,000. Enterprise with multiple sources ranges from $50,000-$90,000. Large-scale systems cost $90,000-$200,000+. Graph database hosting runs $500-$3,000/month.
When should we use GraphRAG vs. standard RAG?
Standard RAG for direct lookups. GraphRAG when answers require connecting information, understanding relationships, or reasoning about complex scenarios. If users ask 'who,' 'why,' 'what if,' or 'how are X and Y connected,' GraphRAG will significantly outperform.
How long to build the knowledge graph?
Initial extraction from 10,000 documents takes 1-2 weeks. Total time to production queries is 6-10 weeks. New documents processed automatically afterward.
Can GraphRAG work with existing RAG?
Yes. GraphRAG enhances rather than replaces standard RAG. Simple queries use fast vector retrieval; complex queries apply the graph.
How accurate is entity extraction?
85-92% precision and 78-88% recall on first pass. Human-in-the-loop validation and multi-pass extraction improve accuracy. Domain-specific fine-tuning pushes above 95%.
Ready to Implement GraphRAG & Advanced Retrieval?
Tell us about your needs and we'll design a custom graphrag & advanced retrieval solution for your business.
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