RAG / Knowledge Base
A GraphRAG system that turns your PDFs, Notion, databases, and corporate documents into a smart AI assistant that answers accurately with source citations — zero hallucinations.
Key facts
Answer quality
Responses are source-grounded, with hallucination control at the architecture level.
Data types
PDF, JSON, CSV, Markdown, Notion, SQL — unified into a single knowledge graph.
Retrieval
Hybrid retrieval: vector search plus graph relationships between entities.
Privacy
Supports local deployment with models running inside your infrastructure.
FAQ
GraphRAG reasons over entity relationships, not only semantic similarity between chunks.
Yes, documents, wiki pages, and SQL sources are commonly integrated and normalized.
After initial indexing, from hours to a couple of days depending on data volume.
Each answer includes source links and context traceability for verification.
Limitations and when it may not fit
If source documents are outdated or contradictory, answer quality degrades.
Complex domain data often requires custom entity schema and extraction rules.
Without regular knowledge-base refresh, system relevance declines over time.
The problem: documents exist — understanding doesn't
Information lives in PDFs, Notion, Slack, Google Drive. No one sees the full picture without manual assembly. Classic keyword search misses connections between documents.
Standard LLMs fabricate facts when they can't find an exact answer in your files. GraphRAG eliminates hallucinations at the architecture level.
How it works
Upload documents
Supports PDF, JSON, CSV, Markdown, Notion data, and SQL databases. The system structures them automatically.
Automatic knowledge graph construction
AI extracts entities and their relationships, creating a semantic graph with 1M+ nodes. All documents are merged into a unified knowledge base.
Ask a question
In plain language — just like asking a colleague. The system understands context and complex multi-part queries.
Get a precise answer with sources
Every answer includes a link to the source document. No fabricated facts — only data from your knowledge base.
Features
Multi-format import
PDF, JSON, CSV, Markdown, Notion, SQL — all formats in one place.
GraphRAG Search
Search by physical relationships between entities, not just semantic word similarity.
Hybrid search
Combination of vector search and graph navigation for maximum relevance.
Fact-only answers
Every answer cites its source. Hallucinations are eliminated at the architecture level.
Local LLM support
Works with local models (Ollama) — your data never leaves your server.
Flexible settings
Graph search depth, similarity threshold, LLM model selection — full control.
Where it's used
Corporate AI assistant — answers questions about internal policies, contracts, and knowledge bases.
Legal & financial firms — fast search across thousands of documents with precise citations.
Technical support — AI answers user questions from the product knowledge base.
Research & analytics — synthesize information from multiple sources without manual review.
Interested in practical GraphRAG experience?
We can discuss answer quality, source grounding, and real-world constraints using practical examples.
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