Data Intelligence Platform
Ontologic Platform
A data intelligence platform that unifies scattered databases, legacy code, and operational documents into a single ontology-based semantic layer, delivering natural-language queries, root-cause analysis, What-if prediction, and watch-agent automation
What agents need is not more data, but “meaning”
Give an LLM agent nothing but a DB schema and an API, and it can read column names and types but never understands the “why.” An ontology fills that gap — we take a closer look below.
Ontologic Platform is a data intelligence platform that builds this ontology automatically from documents and DB schemas, connects it to live data, and delivers natural-language queries, root-cause analysis, What-if prediction, and watch-agent automation in a single flow. The UI is organized into three layers: the Physical Layer (data sources, code, schemas, natural-language queries), the Domain Layer (ObjectTypes and ontology management), and the Dynamic Layer (What-if simulator and watch agents).
Why Ontology, Why Now
As agentic AI begins to work directly with data, the new bottleneck is no longer “access rights” but “knowledge of meaning and causality.”
The Three Core Layers of the Ontology
Ontologic is built from three core layers, Physical, Domain, and Dynamic, forming one continuous flow from physical data assets through the semantic layer to the execution/automation layer.
Physical Layer
Dynamic Layer
Agents Collaborating on the Ontology
When a business process hands an agent a mission, the agent reasons by consulting the relevant nodes in the ontology network, aligns with the business strategy (which is itself part of the ontology), and feeds the result back into the process as an executed action.
This animation shows the agent (the glowing dot) starting in the Data layer, consulting the Assembly Process and Defect Rate nodes in the ontology network, aligning with the quality-first strategy, passing through the Agent layer, and returning to action execution in the Process layer.
End-to-End Usage Flow
From scattered data to proactive action, Ontologic works in four steps
Key Features
Relationship with Ontology Studio
From documents to ontology, from ontology to platform
Where Ontology Studio is an open-source tool that extracts an ontology schema, entities, and relationships from documents and loads them into a Neo4j graph, Ontologic is an enterprise data intelligence platform that connects that ontology to real databases and legacy code and runs natural-language queries, root-cause analysis, What-if, and watch automation on top of it.
Ontologic's ontology construction screen supports document-based generation, which follows the same trajectory as the knowledge graph construction methodology Ontology Studio has built up. Both products share the same goal: making an organization's tacit knowledge explicit as a graph, turning it into a knowledge graph that AI agents can consult.
Integration with Process GPT
Decisions in the ontology, execution in Process GPT
A watch agent continuously evaluates conditions via SQL or What-if simulation, and when a threshold is crossed, the action execution step delegates execution to Process GPT. Process GPT receives the signal and actually launches the predefined business process, so “decision → action” runs as one continuous flow with no human intervention required.
Process nodes in the ontology are also visualized and edited as BPMN diagrams through process-gpt-bpmn-extractor, and processes defined this way are reused by both What-if simulations and watch agents. If Ontologic is the brain that decides “when and why” action is needed, Process GPT is the hands that turn that decision into real work.
Integration with Robo Analyzer
The ontology is only complete once the legacy code has been read
Even a well-maintained DB schema rarely reveals what a table is really for. Ontologic's ingestion engine uses an LLM to analyze legacy code such as stored procedures, functions, and triggers, automatically deriving the meaning of tables and columns and the code–data lineage (WRITES/READS). This legacy code analysis engine uses the same family of reverse-engineering technology as Robo Analyzer.
When ingestion results are not enough — for example, when you need to trace a service's call chain end to end or explore the entire codebase in natural language — you can continue in Robo Analyzer for more precise reverse engineering and feed the results back into Ontologic's ontology.
Comparison Summary
| Attribute | Ontologic Platform | Traditional BI · Data Catalogs |
|---|---|---|
| Data integration | 100+ connectors + legacy code analysis | Structured DB-centric, limited connectors |
| Metadata acquisition | Extracted automatically by an LLM from DDL and procedures | Tagged and documented manually by people |
| Query method | Natural-Language Queries (Text-to-SQL) | Fixed dashboards and reports |
| Semantic layer | Ontology (KPI · Measure · Process · Driver · Resource) | Plain lists of tables and columns |
| Analysis | Root-cause analysis (Granger/VAR) + What-if simulation | Mostly aggregation of historical data |
| Automation | Watch agents launch preemptive action processes | Stops at alerts and report generation |
Get started today!
Unify your scattered enterprise data into a single ontology with Ontologic Platform, and manage everything from queries to prediction and automation in one flow.
Start with the demo video or check out the project on GitHub.
Have more questions?
Want to learn more about Ontologic Platform or have a question? Get in touch anytime.