Lettria vs OpenCrab

Lettria is an enterprise AI platform built around a Graph Context Layer for regulated industries. It unifies structured data and unstructured knowledge into ontology-powered knowledge graphs, giving AI systems a grounded, traceable and trusted context to reason over. Instead of relying on vector search alone, Lettria combines GraphRAG, graph-native document intelligence and production-grade graph agents to deliver answers that can be inspected, verified and defended in compliance-heavy environments. Lettria addresses the limits of vector retrieval: where standard RAG struggles to connect multiple sources, handle structured business data or explain its answers, Lettria's graph approach supports multi-hop reasoning, numerical and temporal reasoning, and full provenance on every response. The platform converts messy documents, tables and SAP exports into queryable knowledge graphs with auto-generated, version-controlled ontologies - built in days, not months, with business experts kept in the loop. Benchmarks show a 30%+ accuracy improvement, up to 400× faster processing than general LLMs, and 81.7% overall accuracy in published evaluations. Lettria offers two entry paths: Knowledge Studio, a graph-native document intelligence platform for knowledge and business teams, and Perseus, an infrastructure for developers building production-grade graph agents. The platform serves finance, healthcare, legal and engineering organizations, with customers including Alfa Laval, WiseCube and Imago Platform. For complex knowledge problems, Lettria also provides forward-deployed engineers, ontology experts and applied AI specialists to take projects from prototype to production.

OpenCrab is an Ontology Graph RAG platform that extracts entities, relationships, evidence, and claims from documents to build source-cited ontology graphs. These graphs can be packaged into reusable ontology packs and queried through graph RAG or MCP-compatible AI tools like Claude, ChatGPT, Cursor, and Codex.

• Ontology-powered GraphRAG delivers 30%+ better accuracy than vector RAG
• Every answer traces back to its source, making AI audit-ready for regulatory review
• Unifies structured and unstructured data, including documents, tables and SAP exports
• Auto-generates governed, version-controlled ontologies in days, not months

• MCP-based integration with Claude, ChatGPT, Cursor, and Codex
• Graph RAG with grounded source citations
• Supports PDF, URL, OCR, Notion, and GitHub ingestion
• Ontology packs can be shared, versioned, and sold via marketplace

• No pricing or free tier is listed on the site, suggesting an enterprise-focused commercial model
• May be overkill for teams with simple retrieval needs where vector search is sufficient
• Requires resourceful team alignment to integrate the graph layer into existing enterprise workflows
• Self-serve onboarding may be steep for non-technical business teams without expert support

• Relatively early-stage platform
• Ontology graph concepts may require a learning curve
• Pricing may be limited for team or enterprise features