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More accurate answers, fewer hallucinations
Combine graph retrieval with vector search to improve reasoning for complex questions and keep answers grounded in your data.
GraphRAG brings structure to retrieval for answers you can trust.
GraphRAG combines vector search with knowledge graphs to reason over entities and relationships within your data, improving accuracy, grounding, and transparency for complex questions.
Higher accuracy on complex queries
Uses entity relationships and graph structure to retrieve the most relevant context.
Fewer hallucinations, better grounding
Answers are anchored in your knowledge graph, not inferred from loosely connected content.
Explainable reasoning, not black-box retrieval
You can trace how information is connected and understand why an answer was produced.
Trusted by leading teams managing large-scale document knowledge.
A new type of RAG: Merging knowledge graphs with vector databases
Goodbye, Hallucinations
Vector search is highly effective at finding relevant content based on semantic similarity. It works well for simple queries and fast filtering across large datasets.
However, vectors operate on proximity, not meaning. They retrieve similar chunks, but do not capture how facts relate, depend on each other, or form a coherent context.
From RAG to GraphRAG: adding structure to retrieval
RAG improves accuracy by grounding LLMs in retrieved documents. But relying on vectors alone limits its ability to handle complex, multi-entity questions.
By adding a knowledge graph layer, GraphRAG introduces structure and relationships, enabling reliable reasoning over connected knowledge.
A structured environment for reliable AI systems
By combining the richness of knowledge graphs with the efficiency of RAG pipelines, GraphRAG creates a structured environment where LLMs can operate reliably.
The result is deeper, more accurate, and traceable answers, enabling the safe use of LLMs, agents, and AI tools on structured, organized enterprise data.
A concrete example: how GraphRAG works in practice
From raw documents to reliable, explainable answers.
Unstructured documents as a starting point
Import unstructured content such as contracts, reports, policies, or product documentation. At this stage, data is fragmented and difficult to query or reason over reliably.
Structuring knowledge into a connected model
Raw documents are analyzed to identify key entities, concepts, and relationships. This step transforms fragmented information into a structured knowledge layer, where facts are explicitly connected and contextualized.
Retrieval with context
When a query is made, the retrieval process is guided by the knowledge graph. Instead of selecting isolated chunks, GraphRAG follows explicit relationships to assemble only the information needed to answer the question accurately.
Generating reliable and explainable answers
Answers are generated from connected, grounded knowledge rather than loosely related content. This results in responses that are more accurate, traceable to source information, and suitable for complex, multi-entity questions.
How Lettria does it
Your texts, documents, and data – structured and unstructured – are turned into a graph.
1. Understand the query
We analyze the user request to identify intent, key concepts, and constraints, creating a structured query for accurate retrieval.
2. Hybrid retrieval
We combine vector search to shortlist relevant documents and graph traversal to capture entity relationships and dependencies.
3. Context consolidation
We merge retrieved passages into a structured, graph-backed context to preserve relationships and improve reliability.
4. Traceable answer generation
We generate answers using the consolidated context, fully traceable to original sources for auditability and compliance.
Trusted by teams using GraphRAG in production
better accuracy
Faster, more accurate outcomes using GraphRAG.
“Our partnership with Lettria has enabled our teams to integrate AI into their workflows. Their collaborative approach and understanding of our challenges has resulted in the implementation of a solution that supports our experts on a daily basis in the analysis of complex technical documents."
Frequently Asked Questions
Currently, Lettria operates on a demo-based access model. To explore GraphRAG's capabilities and see how it can benefit your organization, you can request a personalized demo through the website.
No, Lettria's platform is designed to be user-friendly, allowing users without technical backgrounds to leverage GraphRAG's capabilities effectively.
GraphRAG processes data within a secure environment, ensuring that sensitive information remains confidential and is accessible only to authorized users within your organization.
Yes, GraphRAG is equipped to process complex data structures, including tables. It can convert tabular data into graph formats, enabling more effective querying and analysis.
GraphRAG is versatile and can be applied across various industries, including:
- Healthcare: Structuring patient data for better insights.
- Finance: Enhancing analysis of financial documents.
- Legal: Improving the organization and retrieval of legal texts.
- Engineering: Managing complex technical documentation.
The transformation process involves:
- Document Importation and Parsing: Uploading and preprocessing documents to extract text chunks and metadata.
- Entity Recognition and Linking: Identifying entities and their relationships to construct a knowledge graph.
- Embeddings and Vector Management: Vectorizing text chunks to facilitate efficient retrieval.
- Database Merging and Reconciliation: Combining structured outputs and embeddings into a unified database for RAG applications.
Traditional RAG models rely solely on vector embeddings, which can lead to issues like hallucinations due to a lack of contextual understanding. GraphRAG addresses this by integrating knowledge graphs, enabling the system to comprehend relationships between entities, thereby reducing inaccuracies and improving the relevance of generated content.
Lettria's GraphRAG is an advanced Retrieval-Augmented Generation (RAG) solution that combines the speed of vector-based search with the contextual depth of knowledge graphs. This hybrid approach enhances the accuracy and reliability of AI-generated responses by providing structured context alongside traditional vector embeddings.
Deep expertise on GraphRAG and structured retrieval
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Patrick Duvaut
Head of Innovation
Patrick Duvaut
Head of Innovation
Patrick Duvaut
Head of Innovation
See how GraphRAG improves accuracy and reduces hallucinations
Discover how GraphRAG combines structured knowledge and retrieval to deliver accurate, traceable answers at scale.