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Lettria: GraphRAG Platform for Enterprise GenAI
Lettria turns messy, unstructured data into structured knowledge graphs, powering more accurate enterprise AI through its no-code GraphRAG platform.
Overview
Lettria is an AI-driven knowledge engineering platform built to solve one of enterprise GenAI's biggest headaches: unreliable answers caused by poorly structured data. By combining graph databases with retrieval-augmented generation (GraphRAG), Lettria dramatically boosts the accuracy and traceability of AI-generated responses, giving teams verifiable, source-backed insights instead of black-box guesses.
At the heart of the platform is the Knowledge Studio, a no-code workspace that lets both technical and non-technical users collaborate on transforming raw text and documents into rich ontologies and graph databases. Features like the Text to Graph Pipeline and Ontology Enrichment make it possible to build private GPT chatbots and structured knowledge bases without writing a single line of code, closing the gap between data scientists and business teams.
Organizations across healthcare, retail, finance, and legal sectors already rely on Lettria to unlock value from unstructured data—from structuring patient records at AP-HP to improving product recommendations for Leroy Merlin. Whether it's CRM enrichment, verbatim and speech analysis, or automating investor relations Q&A, Lettria helps enterprises move faster with trustworthy, explainable AI.
Capabilities & Features
- GraphRAG
- Knowledge Graph
- Ontology
- AI
- GenAI
- NLP
- Text Mining
- Text Classification
- No-code
- Data structuring
- Unstructured data
- Knowledge management
Core Features
- GraphRAG for enterprise GenAI accuracy
- Knowledge Studio for unstructured data processing
- No-code collaborative platform
- Text to Graph Pipeline
- Ontology Enrichment tools
- Private GPT chatbot building
Use Cases
- Structuring and analyzing patient data in healthcare settings
- Enhancing product recommendation engines in retail
- Automating verbatim and speech analysis
- Enriching CRM records with structured insights
- Automating Investor Relations Q&A workflows
- Building private, domain-specific GPT chatbots
Best For
- Data scientists
- Knowledge engineers
- Business analysts
- Healthcare professionals
- Finance professionals
- Legal professionals
- Engineers
- CRM managers
- Investor Relations teams
Pros
- •Improves RAG accuracy through graph-based context
- •No-code interface enables cross-team collaboration
- •Provides traceable, verifiable AI answers for trust and compliance
- •Versatile across industries like healthcare, retail, and finance
- •Supports building private GPT chatbots without heavy engineering resources
Cons
- •No public pricing information available, requiring direct sales contact
- •Graph-based approach may involve a learning curve for teams new to ontologies
- •Best results likely depend on quality and structure of input documents
- •May require initial setup effort to build domain-specific ontologies
How to Use
1. Upload or connect your unstructured data sources (documents, text, records) into Lettria's Knowledge Studio.
2. Use the no-code Text to Graph Pipeline to convert raw text into structured graph databases.
3. Build and enrich ontologies to model relationships and context specific to your domain.
4. Deploy GraphRAG to power private GPT chatbots or enterprise GenAI applications with higher accuracy.
5. Query the system and trace every answer back to its source document for full transparency and verification.
6. Collaborate across technical and business teams within the same no-code interface to refine and scale insights.
Frequently Asked Questions
Connect & Contact
Pricing
Lettria does not publish standard pricing tiers; prospective users need to contact the company directly for a custom quote based on their enterprise needs.
Pricing data is provided as a summary. Visit the vendor website for full tier details.