OntoGuard
OntoGuard provides a semantic control plane that governs AI-driven state transitions before they become enterprise commitments. The platform combines decision authorization, evidence retrieval, and audit-proof packet generation to ensure every proposed AI action is authorized, traceable, and safe to release.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Product
Problem
Enterprise AI systems increasingly propose actions—tool calls, record updates, workflow triggers, or customer-facing communications—that can create binding organizational commitments. Without a governance layer at the commit boundary, these proposed state transitions may lack sufficient evidence, delegated authority, or institutional permission, exposing organizations to regulatory, operational, and reputational risk.
Solution
OntoGuard operates as a runtime cognitive control plane that governs the complete event where an AI output attempts to become an organizational consequence. The platform identifies the proposed source and target states, translates AI output into its semantic business meaning, checks institutional admissibility against evidence and policy, and issues an explicit ALLOW, BLOCK, or ESCALATE decision. Every governed run produces a Decision Authorization Packet with buyer-readable PDF and machine-readable JSON artifacts, preserving evidence even when release is withheld. The system applies BM25 lexical retrieval alongside semantic evidence evaluation, and it maintains a proof harness that exports valid evidence or an explicit failure state rather than silent blanks. OntoGuard's governed state transition record binds the exact proposed movement through governing basis, decision result, release state, and whether the protected effect formed.
Target Audience
Primary customers are regulated enterprises in financial services, healthcare, and other compliance-intensive sectors, along with technical buyers, security reviewers, and strategic acquirers who need auditable AI governance for production deployments.
Features
- Decision Authorization Layer that evaluates proposed state transitions against evidence sufficiency, delegated authority, and route authorization before release
- Governed State Transition Record producing buyer-safe proof with decision, scope, evidence, and improvement signals
- Proof Harness with PDF/JSON export, schema-valid packets, confidence tiers, and explicit evidence/citation gap reporting
- BM25 lexical candidate retrieval combined with semantic scope evaluation for evidence relevance scoring
- Machine reason codes such as AUTHORITY_NOT_ESTABLISHED and EVIDENCE_INCOMPLETE for transparent escalation routing
- Strict-Six Offline Audit Credential with six coordinated artifacts including governance.json, governance.pdf, and decision_receipt.json
- Abstention and human-review routing that defaults to SAFE_TEMPLATE or HUMAN_REVIEW when release is not admissible
- No-silent-artifact-failure design ensuring every governed run produces a valid packet or explicit failure-state record