ONTOGUARD AI

Ontology AI Decision Authorization Layer

Ontology AI authorization for high-stakes workflows.
OntoGuard sits between your LLM model and the real world — deciding whether AI output can become action, with proof.

Works with any LLM and any data source. No costly retraining — policies and requirements can change without reworking your model.

What you get each step: ALLOW / BLOCK / ESCALATE + reasons + a replayable proof pack (Governance Report: JSON+PDF, evidence/provenance, policy snapshot + hashes, and decision trail).

📄 Download White Paper 📄 What is OntoGuard?

Why OntoGuard AI Exists

Large language models like GPT, Claude, and Gemini are powerful — but they still make mistakes, break rules, and are hard to explain.

OntoGuard AI doesn’t replace your model. It wraps it — adding an authorization decision and a proof pack before AI output becomes action.

Think of it as a permissioning layer + receipts for AI: you can ship faster because every action is explainable, defensible, and replayable.

Unlike other approaches, OntoGuard doesn’t require retraining when policies, risks, or requirements change. It keeps decisions aligned using structured knowledge + validation + feedback — without touching core model weights.

This is built for AI builders who deploy into high-stakes workflows — where customers demand proof before they approve autonomy.

Key Benefits

💼 Illustrative Use Case – High-Stakes Action Approval

An AI system drafts an action that would trigger a real workflow step (send, approve, file, notify, or execute).

OntoGuard AI produces an authorization decision (ALLOW / BLOCK / ESCALATE) plus a proof pack showing what it relied on and why.

Example deployments include finance, healthcare, insurance, and operations — anywhere an AI output must be defensible before it becomes action.

How It Works

L1 Symbolic Grounding → L2 Semantic Consensus → L3 Alignment Feedback — a three-layer governance stack that keeps decisions auditable as policies and risks change.

1. The AI Evolution

LLM Evolution to Ontology-Enhanced AI

2. Solving the Peak Data Problem

Peak AI Problem and Solution

3. The AI Trust Pipeline

AI Validation and Compliance Scoring

4. Executive Summary

AI Reasoning, Compliance, and Symbolic Trust System Overview 📄 NDA Access Request

Not Just Ontology — Built-in Intelligence

  • 🧠 Adaptive Symbolic Reasoning: Context-aware inferences, not static facts
  • 🔁 Self-Evolving Feedback Loop: Dynamically self-corrects using domain knowledge
  • 📊 Probabilistic Trust Engine: Quantifies uncertainty with semantic validation
  • 🛡️ Logical Compliance Scoring: Enforces interpretable, real-time policy constraints

Patent filed (Apr 24, 2025) — patent pending. OntoGuard weaves dynamic knowledge with validation to produce authorization decisions backed by proof.

Full technical details available under NDA.

Strategic Fit

See It in Action

Explore the live demo or reach out for a deeper strategic discussion under NDA.

Request Enterprise Access 🎯 Launch Live Demo Contact for NDA

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