Connect your complex data with a knowledge graph
Stop sifting through disconnected documents. Lyzr transforms your unstructured data into a powerful, interconnected knowledge graph, allowing you to uncover deep relationships and get precise answers to your most complex questions.
- Answer multi-hop questions across all your data
- Achieve higher accuracy with less hallucination
- Visually explore relationships between key concepts
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From disconnected data to actionable insights
Your documents contain critical relationships that traditional search and RAG systems miss entirely. They struggle to connect the dots between people, processes, and concepts spread across thousands of files. Lyzr’s Knowledge Graph builds a semantic network over your data, so you can stop searching and start reasoning.




Cross-document reasoning
Ask questions that span multiple documents and get a single, coherent answer.




Entity disambiguation
Understand the precise context of every entity, eliminating confusion and errors.




Relationship navigation
Instantly see how different concepts are connected, from organizational structures to project dependencies.
What's live in action
From months of analysis to minutes of discovery
Lyzr’s Knowledge Graph as a Service doesn't just organize your data; it accelerates your ability to use it for decisive action.
- 80% faster insights
Drastically reduce the time it takes to answer complex, relational queries that used to require manual analysis.
- 45% more efficient workflows
Improve process understanding by mapping dependencies and relationships across departments.
- 95% reduction in hallucinations
Get highly accurate, verifiable answers grounded in the structured relationships within your data.
- 10x deeper understanding
Move beyond keyword matching to true semantic reasoning about your most critical information.
Engineered for deep relational understanding
Our platform is purpose-built to extract and leverage the hidden relationships within your unstructured content automatically.
- Automated Entity & Relation Extraction
- Seamless Neo4J Integration
- Hybrid RAG Strategy
- Natural Language Querying
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Frequently asked questions
By creating a unified map of entities and relationships, Lyzr allows you to ask questions that traverse data from multiple documents to form a single, synthesized answer.
You simply need a Neo4J Aura account (a free tier is available). In Lyzr Studio, you connect your credentials (URI, Username, Password), and we handle the rest.
Lyzr’s system is dynamic. As you add new documents, the graph evolves by creating new nodes and relationships, ensuring your knowledge base is always current.
Graphs are optimized for querying relationships. This makes them exponentially faster and more intuitive for answering questions like "how does X relate to Y" compared to rigid table structures.
By mapping processes, tools, and team members as nodes, Lyzr allows you to visualize and query the exact flow of work, identify bottlenecks, and understand dependencies.
Because answers are derived from structured, explicit relationships, knowledge graphs offer significantly higher accuracy and dramatically lower hallucination rates than purely vector-based RAG.
Absolutely. The knowledge graph can serve as a powerful, factual backbone for your agents and other AI applications, providing them with deep, contextual reasoning capabilities.
Any entity-centric domain excels with knowledge graphs, including legal, healthcare, HR, life sciences, and complex technical documentation.
Lyzr uses advanced NLP models to scan your documents, identify named entities (like people, places, and concepts), and understand the semantic relationships between them to build the graph automatically.
For large, highly interconnected datasets, knowledge graphs outperform relational databases by avoiding complex, slow table joins and focusing on direct relationship traversal.
Stop building, Start reasoning
Unlock the powerful relationships hidden in your enterprise data today. Let Lyzr build the connections so you can focus on getting answers.