Lettria

Lettria fills a specific niche: document intelligence for regulated industries. Its ontology-first approach is a real differentiator—many tools claim traceability, but Lettria's graph-based architecture makes it concrete. The ability to build ontologies automatically from data and combine graph+vector retrieval (GraphRAG) is powerful for compliance-heavy use cases. When to pick it: If you're in healthcare, finance, legal, or engineering and need to extract verified knowledge from complex documents with full source grounding, Lettria is worth evaluating. The case studies with AP-HP and Leroy Merlin demonstrate real-world application. The no-code interface also means domain experts can participate directly. When to pass: If you need a free or low-cost tool, or a ready-made general-purpose chatbot, look elsewhere. Lettria requires investment in ontology building and is clearly enterprise-focused. Teams without structured data or willingness to build ontologies may find the learning curve steep. It's also not designed for real-time conversational AI. Compared to alternatives like Glean or Squirro, Lettria's focus on graph-based reasoning and regulated document processing is unique. Glean excels at general enterprise search, Squirro at insight engines, but Lettria's ontology-driven extraction is more aligned with compliance and audit trails. However, those competitors often have more transparent pricing. In practice, Lettria's claims of 30% better RAG accuracy and 60% faster evidence-based research are compelling, but they are vendor-sourced. The platform's success heavily depends on data quality and ontology design. The pilot program suggests a consultative approach, which is both a strength (customization) and a drawback (longer time-to-value).