Most AI projects stall at the data layer, trapped with unstructured documents, siloed databases, and no shared schema. Perseus turns that fragmented data into a knowledge graph your AI can actually reason over.
"We'd stalled on getting our knowledge graph production-ready for months. With Perseus we had a reliable ontology and a working graph pipeline in under a month."
Magnus Helander
Read the case study
Each capability works independently or together, so teams can ship quickly and scale on a reliable graph foundation.
Ontlogies are your graph's blueprint. Auto-generate custom ones from your source documents. Describe your use case in plain language, or upload and extend an ontology you already have (.OWL, .SKOS, .TTL and custom).
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Turn raw documents into a structured network of entities and relationships. Automated text-to-graph pipelines produces standardized, production-ready graphs built to power AI applications.
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Retrieve connected context, not text chunks. Perseus combines vector search with graph retrieval to return entities, relationships, and traversal paths, giving your agents structured knowledge to reason over, backed by explicit source attribution on every result.
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From Fortune 500s to fast-moving startups, customers are building the next generation of AI applications on structured knowledge.
Our fine-tuned model outperforms every frontier model on text-to-graph extraction — reliability, accuracy, and cost.
99.95%
Output reliability
89%
Average F1 score
+9.5 points
vs GPT 5.5
+37.8 points
vs Opus 4.8
Lettria combines benchmark-backed performance with external validation and real-world production deployments.
One shared schema across your entire data. Ontologies unify scattered documents and databases into single entities. While the graph makes complex relationships queryable, the ontology ensures consistency as your domain expands.
Chat history forgets. Graphs remember. An ontology gives your AI agent a persistent world model, allowing it to reason over typed relationships that evolve across sessions, rather than relying on a simple transcript.
Chunk retrieval returns passages that look relevant. An ontology-powered graph returns the relationships themselves, with a citable path from claim to source so the answer can be checked.
Modeling a graph correctly is the most important part. Having the right entities, the right relationships, making them change as your company evolves. When your team wants that expertise embedded, our Forward Deployed engineers and ontologists work alongside you.
Launch your first graph-powered flow quickly, then scale to enterprise throughput with the reliability, support, and infrastructure options production teams need.
Develop
REST & Python SDK
Execute
Serverless compute
Scale
Enterprise ready
Monitor
Console & alerts
Choose the path that fits your team: explore our Knowledge Studio or start building with Perseus.
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