Define the rules, relationships, and constraints that AI agents must follow. Design ontologies visually, validate in real-time, and export production-ready schemas for knowledge graphs, Graph RAG, and agentic systems.
“AI agents are only as good as the semantic contracts they operate under.”
Agentic AI needs explicit domain rules to act reliably.
Graph RAG systems need structured relationships to reason over.
Knowledge graphs need governed schemas to scale without drift.
AI agents hallucinate because they lack formal domain understanding
Legacy ontology tools have steep learning curves and unintuitive interfaces
Validation happens too late — after agents have already acted on bad data
No way to define enforceable semantic contracts for AI systems
Designing semantic contracts for AI shouldn't feel like configuring research software from 15 years ago.
OntoBoom is where you design the semantic contracts that govern AI behavior. Define domain models visually, validate constraints in real-time with AI assistance, then export production-ready schemas to your agents, knowledge graphs, and RAG pipelines.
Drag-and-drop classes, properties, and relationships. See your ontology structure at a glance with an intuitive graph editor.
Accelerate your workflow with intelligent suggestions, automatic error detection, and instant fixes. Build ontologies 10x faster.
Real-time validation catches issues as you design. No more discovering broken ontologies after deployment.
Work with a clean JSON representation that's easy to version control, diff, and integrate with your existing tools.
Import from DDL/SQL, CSV, JSON, XML Schema, Turtle, JSON-LD, and RDF/XML. Use AI to extract ontologies from plain text, PDF, or Word documents.
Export to Turtle, JSON-LD, RDF/XML, OWL, SHACL shapes, and Cypher for triple stores and graph databases.
Programmatic access to your ontologies. Generate API tokens for external apps, pipelines, and automated workflows.
Connect Claude Desktop and AI assistants directly to your ontologies via Model Context Protocol.
Databases store technical structures, not clear business meaning — and AI agents need meaning to reason correctly.
For example, a table might have a column stat_cd = A, which a human knows means "Active customer," but an agent has to guess. An ontology defines this explicitly: Customer hasStatus Active.
By mapping data to an ontology, you give agents a clear semantic layer instead of messy schema details, making them more accurate, reliable, and safe in real enterprise systems.
Without that semantic layer, agents guess; with it, they understand — and understanding is what turns AI from a demo into real enterprise value.
Export your ontology as an OPS Package — a self-contained bundle that gives AI agents structured understanding of your data, ready to plug into any framework.
ontology.ttl
Your full OWL ontology in Turtle format — classes, properties, relationships, and constraints.
mapping.json
Database-to-ontology mapping rules that translate your schema into semantic concepts.
tool-schema.json
AI-ready tool definition — drop it into LangChain, OpenAI function calling, or any agent framework.
shapes.ttl + ops.json
SHACL validation shapes and a manifest with SHA-256 checksums for integrity verification.
Point OntoBoom at your PostgreSQL, MySQL, or SQL Server instance. We capture a schema snapshot — table names, columns, types, and relationships.
Auto-map with deterministic matching or AI suggestions. Fine-tune rules manually — link tables and columns to classes, data properties, and object properties.
Download the OPS package and plug it into your AI stack. Agents use the tool schema to query your database with semantic concepts instead of raw SQL — no more hallucinated column names.
# Works with any agent framework
from langgraph.prebuilt import create_react_agent
agent = create_react_agent(llm, tools=[semantic_query])
Build the semantic schema that powers retrieval-augmented generation. Define entities and relationships that give your LLM structured context to reason over.
Design enterprise knowledge graphs with proper class hierarchies, property constraints, and data validation rules before loading your first triple.
Encode business rules and domain constraints that AI systems must respect. Turn implicit knowledge into explicit, enforceable schemas.
Give AI agents a formal understanding of your domain. Model concepts, relationships, and constraints that help agents make better decisions.
Free users get 5 AI credits to explore the full power of AI Copilot. Upgrade to Pro for unlimited projects, ontologies, and the ability to purchase additional AI credits.