Specialist Consultancy · Enterprise Ontology · Agentic AI

We build the ontology layer
your AI agents are missing.

OntoArc designs production-grade ontology architecture for enterprise AI — transforming legacy platforms into explainable, auditable, and self-healing agentic systems.

Enterprise AI agents are failing in production.

The reason is architectural, not algorithmic.

The Problem

AI agents without ontology architecture make contextually insane decisions — technically correct outputs that violate business logic, compliance rules, or domain constraints.

The Gap

Vector databases and RAG pipelines retrieve information but don't enforce meaning. Knowledge graphs store relationships but don't validate agent behaviour. The semantic layer between your data and your agents is missing.

OntoArc

We extract the rules. We model them as OWL ontologies. We deploy them as production-grade guardrails for AI agents. Explainable. Auditable. Self-healing.

One firm. One specialisation.

We don't do general AI consulting. We solve one class of problem: building the ontology architecture layer that makes enterprise AI agents production-ready.

01

Extract

Pull business rules from proprietary rule engines, Java, SOAP systems, legacy databases.

02

Model

Encode as OWL ontology — explainable, auditable, versionable, machine-readable.

03

Deploy

Run on agentic AI via MCP — self-healing, policy-aware, production-grade.

See the ontology firewall in action

AI agents without semantic guardrails make contextually insane decisions. Toggle OntoGuard to see the difference.

OntoGuard OFFOntoGuard ON
$ Agent query:

Is this applicant eligible for the housing benefit?

⚠ No validation — dangerous output

Yes, the applicant qualifies based on income threshold.

Issue

Agent ignored residency requirement, dependant status, and active employment disqualification. Technically correct on one criterion, contextually wrong on three.

OntoGuard is open source → GitHub

150+

Published Research Articles

150K+

Article Views on Medium

9

Open Source Projects

40+

GitHub Stars Across Projects

150+ articles. 4 structured tracks. Start anywhere.

Our research distilled into a curriculum. Each track builds on the last — or jump in where you need.

Track 01

Foundational Ontology

What ontologies are, why AI needs them, and how to build your first one. From OWL basics to production integration.

  • Building Your First Ontology: A Hands-On Tutorial
  • Understanding Ontology: The Brain Your AI Has Been Missing
  • From Protégé to Production: Integrating Your Ontology with Python
Explore track
Track 02

Agentic Infrastructure

MCP servers, A2A protocols, and the agentic mesh. How to connect AI agents to enterprise data with semantic precision.

  • MCP + A2A + OWL Ontology: The Agentic Mesh
  • Universal Agent Connector: MCP + Ontology
  • From Ontology to Production: Self-Healing Multi-Agent System
Explore track
Track 03

OntologyOps

Production lifecycle management for semantic layers — versioning, testing, firewalls, and self-healing architectures.

  • Beyond Extraction: Building Production-Grade OntologyOps
  • The Ontology Firewall: Why Enterprise AI Agents Are Failing
  • OntoGuard: I Built an Ontology Firewall in 48 Hours
Explore track
Track 04

Industry Modernisation

Real-world migration patterns from legacy platforms to AI-native infrastructure in government, finance, and healthcare.

  • Microsoft vs Palantir: Two Paths to Enterprise Ontology
  • Palantir Foundry Ontology: How It Works and Where It Falls Short
  • The $47 Billion Problem: Contextually Insane AI
Explore track

Packaged solutions. Open-source core. Enterprise-grade delivery.

Each kit bundles our repos into a production-ready engagement.

OntoGuard Enterprise

$5,000 setup + $2,000/month

Production-grade ontology firewall that prevents AI agents from making contextually insane decisions. Validates agent outputs against OWL-encoded business rules in real-time. Role-based access, constraint checking, full audit trail.

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Legacy-to-Logic Migration

$12,000–$25,000 per engagement

End-to-end transformation of legacy database schemas and rule engines into AI-ready OWL ontology infrastructure. Extracts business rules from proprietary platforms, maps them semantically, generates MCP-compatible ontology with self-healing capabilities.

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Power BI Ontology Unlock

$8,000 per engagement

Transforms existing Power BI data models into AI-ready ontologies. Extracts hidden business rules from DAX measures, generates OWL ontologies, creates MCP-compatible semantic contracts for AI agents. Unlocks the intelligence trapped in your dashboards.

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Case studies

Anonymised delivery outcomes from enterprise and government engagements.

Open Source / AI Security

OntoGuard: Production-Grade Ontology Firewall for AI Agents

Built and open-sourced a production-grade ontology firewall that validates AI agent actions against OWL-encoded business rules in real-time. Enforces role-based access control, amount constraints, time windows, and compliance rules. Provides human-readable explanations for every blocked action and a full audit trail for regulatory compliance.

8

GitHub stars

3

Enterprise domains demonstrated

5.2K

Views on launch article

OWL OntologySHACL ValidationPythonAI Governance

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