The AI doesn't guess.
It understands your domain completely.
Data Graphs' schema-centric knowledge graph gives AI a total understanding of your data structures and relationships. The result: precise GQL queries, grounded answers, and zero hallucination. Combined with vector retrieval for unstructured content, this is hybrid RAG that works out of the box, with no prompt engineering and no retrieval tuning required.
Vector finds similar. Graph finds true.
Your domain model is the AI's brain
In Data Graphs, the domain model (schema) is not just a constraint on your data. It is a complete, machine-readable description of what your data means: the entities, their properties, their relationships, and the rules that govern them.
When the AI receives a question, whether from a human or an agent, it reads this schema and understands the domain the same way an expert would. It knows what a "Product" is, how it relates to an "Ingredient," what a "Registered Formulation" contains. It writes precise, correct GQL queries to find the answer, not approximate text matches.
This is why it works out of the box. You model your domain, import your data, and the AI just works. No prompt engineering. No retrieval tuning. No hallucination mitigation. The schema does the heavy lifting.
No prompt engineering. No retrieval tuning. Works out of the box.
Two retrieval paths, one seamless answer
Graph retrieval (GQL)
For structured questions: "Which products contain this ingredient?", "Show me all registrations expiring this quarter," "What is the relationship between X and Y?" The AI reads the schema, writes a precise GQL query, and returns exact, complete, correct results.
Vector retrieval (embeddings)
For unstructured content: documents, rich text, notes, PDFs, media transcripts. When the answer lives in prose rather than structured data, vector search finds semantically similar content and surfaces it alongside the graph results.
The hybrid combines both seamlessly. The AI decides which retrieval path(s) to use based on the question. Responses cite their sources, whether structured graph data or unstructured content.
Graph and vector are complementary. Vector excels at unstructured semantic search. Graph excels at structured relational reasoning. Data Graphs gives you both.
MCP-native from the ground up
Data Graphs exposes its capabilities through built-in Model Context Protocol (MCP) services. AI agents don't need custom integration code. They connect via MCP, read your domain model, and start querying your governed data immediately.
Every query goes through the same governance layer: role-based access control, audit trails, and business rules. The same context layer that serves your copilots also serves your automated workflows.
Purpose-built for the gap others leave open
See the AI in action on your data
Model your domain. Import your data. Ask a question. Watch it work.