Your organization's knowledge is scattered across documents and databases alike
“Which customers received the product that kept having quality issues, and how much did those customers claim?” — The hardest questions are usually the ones whose answer lives in no single place.
Regulations sit in PDFs, manufacturing execution records in PostgreSQL, customers, deliveries, and claims in MySQL, and none of them know about each other.
Ontology Studio solves this with an ontology, without moving anything. Documents and operational databases stay where they are and are simply connected,
and the tool designs domain-entity-level classes and relationships complete with join keys on its own, guided by your build intent and Golden Questions.
What moves is not the data but only names and relationships; values are fetched from the source at query time.
The finished graph is exposed as an MCP server and works with any AI agent, including Claude and Process GPT.
Demo 1 Upload documents, auto-design the ontology schema, and answer with Graph RAG
Demo 2 Two databases on different engines, unified into one ontology with zero replication
Key Features
Connect → recover meaning → design entities → load and bind → relate → query: the same flow, regardless of source type
The value of an ontology lies in the ‘next question’
If you build a table per query or chunk and index documents, every new question calls for a new design, and the maintenance burden grows with the number of questions. Model with entities, and a new question simply follows the relationships that already exist. The value of an ontology is not that it answered one question, but that it can answer the next one.
How It Works
All interpretation of the physical layer (document structure, tables, columns, joins) is finished at build time. At query time, only the ontology is consulted.
1. Connect sources
2. Recover meaning
3. Design entities
4. Load · Bind
5. Validate & query
Graph RAG vs. Vector Search
| Attribute | Ontology Studio (Graph RAG) | Typical vector search RAG |
|---|---|---|
| Retrieval method | Graph traversal follows connected clauses and records | Only top chunks by similarity, retrieved individually |
| Context connectivity | Every relationship between clauses and entities is captured | Relationships between chunks are hard to discern |
| Evidence | Referenced nodes and sources provided explicitly | Limited source traceability |
| Accuracy | High accuracy grounded in structured knowledge | Relatively higher risk of omissions and hallucinations |
| Schema construction | Auto-designed from documents and DBs & self-improving | No concept of a schema |
| Agent integration | Standard integration via MCP (Claude, etc.) | Separate integration required |
Related Solutions
A Knowledge Graph Never Works Alone
Ontology Studio is the design tool that makes an organization's knowledge explicit as a graph. Its output runs on the execution server of Ontologic Platform for analysis and automation, becomes, via MCP, the knowledge graph that Process GPT agents consult while doing their work, and gives the design and implementation agents of Robo Architect their grounding in the domain.
① Ontologic Platform
Design in Studio, execute on the Ontologic server
Ontology Studio plugs in as the ontology design module of Ontologic Platform. The schema Studio creates (classes, relationships, join keys) and its data source mappings connect directly to the Ontologic execution server, where natural-language queries, root-cause analysis, What-if simulation, and watch automation run on top of them.
Because design and execution share the same ontology, fixing the schema is immediately reflected in execution results, and the zero-replication binding carries straight through, so analysis runs on operational data with no separate ETL (Zero-ETL).
② Process GPT
The knowledge graph that agentic AI consults
The built ontology is exposed as an MCP server, and Process GPT deep agents, while carrying out their work,
use ontology_query to explore this knowledge graph in real time.
They cite regulatory clauses and operational data together, and decide only on the basis of knowledge made explicit in the ontology, completing hallucination-free business automation.
In an AI-Native Enterprise where AI agents decide and act on their own, what is most lacking is not model performance but “what to base decisions on.” The ontology states that grounding in the organization's own language, and Process GPT executes real work on top of it. This combination, where designed knowledge is executed directly, is the most powerful scenario.
③ Robo Architect
A knowledge graph that carries through to design and implementation
The domain knowledge graph built by Ontology Studio becomes the knowledge graph consulted by the design and implementation agents of Robo Architect. Concepts and relationships extracted from documents and operational data ground the design of Aggregates and domain events, so the AI generates code with an accurate understanding of the domain.
The two products move freely back and forth — Ontology Studio's knowledge flows into Robo Architect's specifications, and the models Robo Architect designs accumulate back into the graph. Knowledge and specifications circulate on one track.
Get started today!
Ontology Studio is available as open source. With just a few documents or database connection details, you can easily build your own knowledge graph.
Runs in Docker or locally.
Have more questions?
Want to learn more about Ontology Studio or have a question? Get in touch anytime.