Structuring Enterprise Knowledge: Lettria Unveils Two AI Modules for Document Intelligence

As part of the €4 million LettRAGraph initiative funded by France 2030, Lettria is rolling out two new modules aimed at helping regulated industries make sense of complex, unstructured documents. The focus is clear: structure and traceability over black-box answers. These tools are designed to work within real operational environments: multilingual, regulated, and often full of legacy content.

Available through Lettria’s no-code platform, both modules address the core challenge many enterprises face: how to unlock the value of their text-based data while staying in control of accuracy, compliance, and internal logic.

Turn Raw Documents Into Structured Graphs

The first module helps organizations generate and maintain business-ready knowledge graphs from text-heavy sources. These graphs aren’t generic. They're built around the company’s own terminology and data models, then kept up to date with minimal manual work.

Rather than limiting itself to basic named entities, the system can surface domain-specific relationships from contracts, policies, reports, or regulations. Think of statements like “Entity A delegates responsibility to Entity B under condition C” — captured and stored as structured, queryable data. The module can also generate or enrich ontologies by pulling from internal documentation and external standards.

It’s designed with specific industries in mind. In insurance, for instance, the tool can link policy clauses to standard product categories, track relationships between claims and legal parties, or support fraud analytics. In legal teams, it turns case law and contracts into usable data by aligning clauses with internal templates or known risks. Financial organizations can model exposure and map obligations across counterparties. Healthcare providers can connect diagnoses, procedures, and treatments into patient-centered views, compliant with formats like HL7 and SNOMED.

Ask Smarter Questions With GraphRAG

The second module, called GraphRAG, brings conversational access to enterprise knowledge. But instead of relying on general-purpose chatbots, it uses a hybrid retrieval method: semantic search for flexibility, and graph queries for precision and traceability.

What this means in practice is that employees can ask questions in natural language, even complex, context-dependent ones, and get responses grounded in the company’s own data. Every answer includes source references. Governance rules and role-based permissions can be applied directly in the logic, so results stay compliant.

It works across different content types (text, tables, structured records) and can plug into systems already in place, such as Neo4j or GraphDB. Whether it's legal, compliance, claims management, or procurement, the system helps surface specific obligations, exceptions, or risk factors buried deep in internal documentation.

In regulated contexts like public health or government, where multilingual legal corpora are the norm, the tool supports cross-language use with the same guarantees on source traceability and response integrity.

Built to Deploy, Not Just to Demo

Both modules are delivered via Lettria’s platform, with optional on-premise or sovereign deployment (OVHcloud supported). Role-based access controls and GDPR compliance are built in.

The goal isn’t experimentation. Each deployment starts with a focused 12-week program: identifying the use case, evaluating the corpus, tuning the extraction layer, aligning ontologies, and supporting internal validation with real users.

Customization is part of the process. Whether it's integrating proprietary taxonomies or working with legacy graph models, the system is made to fit enterprise constraints, not the other way around.

A More Transparent Approach to AI

What sets this apart from traditional generative AI is clarity. Lettria focuses on structure, alignment, and context. These modules help organizations work with language in a way that’s interpretable, scalable, and grounded in the real constraints of their industry.

For teams dealing with dense documentation, the promise is simple: faster access to relevant information, higher confidence in the answers, and full control over how data is extracted, structured, and used.

If you’re exploring how this could fit into your environment, request a demo, and let us show you how we can help your organization.