OntoGuard AI is the Semantic Control Plane for enterprise AI.

Your AI already produces outputs. OntoGuard measures their behavior, governs their autonomy, compresses human review, creates proof, and turns governed outcomes into clean learning signals.

Start without uploading internal documents. Send us 50–500 prompts, responses, or AI logs. OntoGuard shows what is risky, overconfident, inconsistent, review-heavy, ready for autonomy, or useful as training signal.

For high-stakes workflows, OntoGuard can also return ALLOW, BLOCK, or ESCALATE before release and generate a Decision Authorization Packet with evidence, uncertainty, audit hashes, routing, and improvement signals.

Protected by U.S. Patent Application 19/444,521 — Track I prioritized examination granted May 2026.
Production today: AI output behavior assessment, LLM output authorization, and L3 training-signal export. Extension path: tool calls, memory writes, ontology changes, policy mappings, and multi-agent handoffs through API integration, with full enforcement middleware rolling out next.
AI Output Risk Scan No Internal Documents Required Review Burden Reducer Decision Authorization Packet Learning Signal Export
Before OntoGuard: teams reviewed AI outputs manually, without consistent risk, uncertainty, proof, or learning-signal structure.
After OntoGuard: outputs can be measured, routed, improved, and escalated before they become business action.

Decision Authorization is a premium capability inside the broader Semantic Control Plane. Last updated: May 20, 2026.

Run an AI Output Risk Scan Get a Demo Proof Pack Download Sample Proof Packet
1. Send outputs/logs 2. Detect risk + review burden 3. Export proof + learning signals 4. Expand to authorization

Start Without Uploading Internal Documents

OntoGuard does not need your legal files, policy manuals, claims folders, contracts, or internal procedures to deliver first value. Send the AI outputs your system already produces. OntoGuard analyzes the behavior itself.

What is risky?

Detect unsafe recommendations, customer-impacting language, unsupported claims, and outputs that should not move forward without review.

What is overconfident?

Find outputs that sound certain when uncertainty, escalation, evidence gaps, or human judgment should be preserved.

What needs review?

Route only the outputs that need expert attention, with reason codes and reviewer context.

What is ready for autonomy?

Classify workflows by readiness: draft only, recommend with review, release low-risk outputs, escalate sensitive outputs, or block unsafe outputs.

Where are reviewers wasting time?

Identify avoidable review volume, repeated escalation patterns, and outputs that can be handled through lighter review paths.

What can improve the model?

Turn governed outputs into clean learning signals: good examples, blocked examples, escalation examples, rewrites, and reviewer-labeled outcomes.

What OntoGuard Tells You

OntoGuard analyzes AI behavior from outputs, logs, and proposed actions — then turns that behavior into operational intelligence.

What is risky
What is overconfident
What needs review
What should be blocked
What is ready for autonomy
Where reviewers are wasting time
Which outputs make good training examples
How the workflow compares to governance benchmarks
How model behavior changes over time

Three Ways to Start

OntoGuard can start as a lightweight assessment, a vendor proof pack, or a review-compression pilot — without requiring a full enterprise deployment.

Lowest Friction

AI Output Risk Scan

Send us 50–500 AI outputs. OntoGuard shows what is risky, overconfident, inconsistent, review-heavy, ready for autonomy, or useful as training signal.

Run Risk Scan
For AI Vendors

AI Demo Proof Pack

Turn your AI demo into enterprise-ready proof: governed outputs, risk signals, human-review routing, auditability, and improvement signals.

Request Demo Proof Pack
Fast ROI

Human Review Burden Reducer

Find which AI outputs actually need expert review, which can be lightly reviewed, and where expensive reviewers are wasting time.

See Review Compression

Run an AI Output Risk Scan

Send 50–500 AI prompts, responses, or API logs. OntoGuard returns a behavior risk map showing which outputs are safe, risky, overconfident, inconsistent, review-heavy, ready for autonomy, or useful as clean learning signals.

No internal document upload required. No production deployment required. No model retraining required. Start with the outputs your AI already produces.
50–500 AI outputs analyzed Prompts, responses, API logs, demo outputs, or exported workflow samples.
Risk, overconfidence, and inconsistency map Shows unsafe language, unsupported claims, contradictory outputs, and confidence mismatch.
Review burden and routing analysis Identifies which outputs need expert review, light review, rewrite, escalation, or block.
Autonomy readiness tier Classifies workflows as draft-only, recommend-with-review, low-risk release, escalate-sensitive, or block.
Training-signal candidates Surfaces good examples, blocked examples, rewrite candidates, and reviewer-labeled learning signals.
Sample proof packets for high-risk outputs Generates buyer-readable proof artifacts for selected outputs that need audit or executive review.
Built for: Model Risk Management AI Governance / Responsible AI Compliance Engineering Internal Audit Regulated Workflow Owners AI Product Teams

Request AI Output Risk Scan

This form uses a static form handler for lead capture. Backup: email OntoGuard directly. No deployment is required to start a shadow-mode scan.

Human Experts Are Not Replaced. They Are Focused.

OntoGuard does not eliminate domain experts. It reduces when and how they are needed.

Instead of asking experts to review every AI output from scratch, OntoGuard packages the decision, evidence, gaps, uncertainty, policy scope, and reason codes into a reviewable packet. The human reviewer becomes an exception authority, not a full-time AI babysitter.

The goal is not “no humans.” The goal is fewer blind reviews, faster escalation, clearer accountability, and better use of expensive expertise.

In a pilot, OntoGuard measures escalation rate, average review minutes, reviewer role, false-approval reduction, and cycle-time impact so the cost of human review becomes visible instead of assumed.

How to Read an OntoGuard Decision

Read the packet as a release decision, not as a standalone percentage. The score contributes to the decision; it does not replace evidence, policy scope, benchmark status, or human accountability.

AI proposes an actionOutput, recommendation, workflow step, or governed state change.
↓
OntoGuard checks control signalsEvidence, policy, risk, uncertainty, citation gaps, and traceability.
↓
Decision API returnsALLOW, BLOCK, or ESCALATE before release.
↓
Packet records whyReason codes, scope, evidence IDs, audit hashes, and review route.
↓
Human acts only when neededReviewers handle exceptions with context instead of re-reading everything from scratch.

The Score Is Not the Decision

OntoGuard is not a scoring dashboard. It is a semantic control plane for governing AI-driven state transitions. It can return ALLOW, BLOCK, or ESCALATE when an AI output or action requires release control.

81% does not mean yes.

OntoGuard does not approve AI actions because a trust score is 81%, 85%, or 95%. The score is one signal inside a governed decision process.

OntoGuard evaluates evidence coverage, uncertainty, citation gaps, policy scope, risk, benchmark status, and human-review requirements before returning ALLOW, BLOCK, or ESCALATE.

High score, still ESCALATE.

A high score can still result in ESCALATE if evidence is incomplete, citations are pending, risk is unresolved, or human review is required.

OntoGuard does not tell you “81% means yes.” It tells you whether the AI output is safe to release, should be blocked, or must be routed to a responsible reviewer — with a proof packet explaining why.

What happens when the score changes?

OntoGuard treats every AI decision as a governed moment in time. A score is never evaluated alone. The system records the prompt, proposed output, evidence state, scope, uncertainty, gaps, decision, reviewer route, and audit hashes for that specific run.

If the evidence improves later, the decision can be re-run and compared. OntoGuard’s value is that each decision is traceable, reviewable, and repeatable — not that one percentage is permanently “true.”

Different workflows can have different release criteria.

Different branches, verticals, and workflows can use different policy profiles, evidence requirements, thresholds, approver roles, and escalation rules. A 79% decision in a low-risk workflow may be handled differently from a 95% decision that conflicts with the current business objective, policy scope, or regulatory context.

OntoGuard evaluates the decision against the active workflow criteria — not against a universal percentage.

What You License Today

OntoGuard can start as a lightweight output scan and expand into runtime release control, proof packets, training-signal export, cognitive drift tracking, and agentic governance.

Core capabilities are live today: AI Output Risk Scan, LLM Output Authorization, proof packets, review routing, and L3 Training Signal Export.
Lowest Friction

AI Output Risk Scan

Analyze 50–500 prompts, responses, or logs to identify risk, overconfidence, inconsistency, review burden, autonomy readiness, and training-signal candidates.

For AI Vendors

AI Demo Proof Pack

Turn an AI demo into enterprise-ready proof: governed outputs, risk signals, review routing, auditability, and improvement signals.

Fast ROI

Human Review Burden Reducer

Identify which outputs need expert review, which can use lighter review, and where expensive reviewers are spending avoidable time.

Engineering / Risk

Decision Authorization API

Runtime API that returns ALLOW, BLOCK, or ESCALATE with release status, routed_to, business effect, reason codes, evidence references, and audit identifiers.

Compliance / Legal

Decision Authorization Packet

Portable PDF and JSON proof artifact showing how a high-stakes AI output was authorized, escalated, or blocked before release.

Learning Systems

Governed Training Signal Export

Converts allowed, blocked, escalated, rewritten, or reviewer-labeled outcomes into clean improvement signals.

AI Governance

Cognitive Drift Observatory

Tracks how AI behavior, risk, confidence, routing pressure, and governance readiness change over time.

Platform / DevOps

Agentic API Integration Path

API integration path for tool calls, memory writes, ontology changes, policy mappings, and multi-agent handoffs, with full enforcement middleware rolling out next.

1. Output Risk ScanAnalyze outputs and logs.
2. Review CompressionRoute only what needs judgment.
3. Proof PacketsExport PDF + JSON evidence.
4. Decision AuthorizationALLOW / BLOCK / ESCALATE before release.
5. Learning SignalsTurn governed outcomes into improvement data.
6. Drift + Agentic ControlTrack behavior change and govern actions.
All artifacts are schema-stable and exportable. No silent failures. Every scan, decision, or escalation can produce a complete, auditable record.

How OntoGuard Is Different

Monitoring tools observe logs. Evals test models before deployment. GRC tools document controls. OntoGuard analyzes AI behavior itself, identifies risk and review burden, governs autonomy when needed, and turns each governed run into proof and learning signals.

Capability
Monitoring Tools
Evals / Testing
Traditional GRC
OntoGuard
Measures AI behavior from outputs/logs without internal document upload
Limited
Sample-based
✗
✓
Identifies overconfidence and unsafe action language
Limited
Partial
✗
✓
Compresses human review through reason-coded routing
✗
✗
Manual
✓
Produces buyer + machine-readable proof
Limited
Limited
Manual
✓ Full
Produces clean learning signals
✗
Limited
✗
✓
Benchmarks governance readiness
✗
Partial
Manual
✓
Tracks cognitive drift
Logs only
Point-in-time
✗
✓
Can enforce ALLOW/BLOCK/ESCALATE for high-stakes outputs
✗
✗
Limited
✓
Agentic/memory API integration path
✗
✗
✗
✓ API path / roadmap enforcement

Measurable Business Impact

OntoGuard is not only an audit story. In a pilot, its artifacts map to the buyer baseline inputs needed to calculate review minutes saved, escalation-rate reduction, false-approval prevention, faster cycle time, and safer launch velocity.

Opex Takeout

  • Reduce paid review minutes
  • Lower escalations and rework loops
  • Automate evidence packaging and QA checks
Escalation rate ↓Review minutes ↓AHT ↓

Loss Prevention

  • Prevent wrong approvals and policy leakage
  • Catch issues before incidents or rollbacks
  • Route risky decisions to accountable review
False approvals ↓Incidents ↓Fraud exposure ↓

Working Capital

  • Shorten claims, disputes, exceptions, and close cycles
  • Improve denial prevention and collections workflows
  • Attach proof at the moment of decision
Cycle time ↓Denials ↓DSO ↓
Assumption-safe ROI: hard-dollar calculations appear only when buyer-supplied baselines exist. No invented savings, no fake benchmarks.

Ontology Is Not Just a Model — It Is the Control Plane

Most ontology work stops at knowledge representation. OntoGuard takes ontology into runtime: it grounds every AI proposal in enterprise objects, relationships, policies, and evidence, then uses that semantic layer to authorize, block, or escalate the proposed state transition — with full traceability and learning feedback.

This is what makes OntoGuard different: ontology becomes the active reasoning and authorization layer, not just a static data model.

Runtime Cognitive Control Plane for State-Transition Governance

OntoGuard is the Ontology AI and Semantic Layer for enterprise AI: a runtime cognitive control plane, Reasoning Layer, and Cognition Layer for state-transition governance. Modern AI systems no longer just answer prompts; they call tools, write memory, update knowledge graphs, trigger workflows, hand work to other agents, and generate training signals that change future behavior.

From LLM outputs and training signals to tool calls, memory updates, ontology changes, policy mappings, and multi-agent handoffs (via integration), OntoGuard turns every proposed state change into a governed, auditable decision with evidence, routing, risk, uncertainty, arbitration transparency, audit hashes, and improvement signals.

OntoGuard governs the commit boundary. It asks whether a proposed AI state transition is authorized, traceable, evidence-backed, and safe to release before the change touches a customer, workflow, regulator, record, or downstream system.

The result is a Governed State Transition Record — buyer-safe proof showing decision, scope, evidence, and improvement signal.

The Decision Authorization Layer is the high-stakes release-control capability inside the broader Semantic Control Plane.

Regulatory compliance is not the ceiling. It is the first high-value use case for a broader enterprise control plane.

Zero Hard Gates. Always Export. Evidence Never Blank.

Regulated buyers need governance that is resilient under ambiguity. OntoGuard is designed so uncertainty does not erase the audit trail.

Zero Hard Gates

Hard failures are converted into explicit abstention, SAFE_TEMPLATE, or HUMAN_REVIEW routing so the governance record still exists.

Always-Export Artifacts

Every governed run is expected to produce a buyer-readable PDF and complete JSON packet, even when the answer is withheld.

Evidence-Never-Blank

If evidence is sparse, OntoGuard records fallback reasons, provisional review anchors, uncertainty, and next human step instead of silently emitting an empty proof trail.

What the Runtime Control Plane Gives You

How It Works — The 3-Layer Semantic Governance Stack

OntoGuard is not just ontology lookup and not just scoring. It uses ontology throughout the governance path to connect an LLM output to policy, evidence, scope, trace, human review, and learning feedback.

Executive Summary

OntoGuard uses a three-layer system to turn raw AI outputs into governed, auditable decisions. Layer 1 grounds outputs to real enterprise objects and rules. Layer 2 checks accuracy, risk, uncertainty, and compliance. Layer 3 turns every approved or corrected decision into a learning signal that improves future retrieval, policy, and agent behavior.

Result: fewer incidents, faster audits, clearer human review, and safer autonomy without changing model weights.

Full Technical Detail

The technical view below shows how L1 symbolic grounding, L2 semantic consensus, and L3 alignment feedback produce the Decision API, Governed State Transition Record, evidence pack, triad, audit credential, and improvement signal.

L1

Symbolic Grounding

Normalizes the governed prompt and response into clause hits, evidence IDs, retrieval IDs, checksums, regulation/domain scope, and a buyer-readable symbolic trace.

  • Ontology-grounded concepts
  • Clause and fallback hits
  • Evidence pack and provenance
L2

Semantic Consensus

Compares compliance, accuracy, risk, uncertainty, hallucination status, and agent disagreement before deciding whether release is safe.

  • Trust and uncertainty signals
  • Cold / Heat / Mercury triad
  • ALLOW, BLOCK, or ESCALATE routing
L3

Alignment Feedback

Turns governed outcomes into reusable improvement signals for retrieval, policy, alignment, training data curation, and future agent decision quality.

  • Gold example candidates
  • Human-review outcomes
  • Closed-loop training signal export

From Output Governance to State-Transition Governance

Agentic systems don’t just answer — they change state. OntoGuard already governs LLM outputs and is extending the same rigorous control to tool calls, memory, and agent actions. The question now is: is this proposed transition authorized, traceable, and safe to commit?

Old AI Governance Unit
Future Governance Unit with OntoGuard
Model output
Governed decision / state transition
Prompt / response pair
Before → Proposed → After state
Hallucination check
Uncertainty-aware authorization
Logging
Audit credential + improvement signal
Human review
Routed decision workflow
Fine-tuning data
Governed training signal
Agent action
Tool-call / memory / ontology authorization
Static governance weakens when AI systems use tools, maintain memory, and update knowledge. OntoGuard makes each proposed transition explicit before it becomes business state.
Explore the 7 Governable Events See Ontology as Enterprise Reality View the Financial Services Pilot

What We Govern: The 7 State Transitions

Seven event types can become ALLOW, BLOCK, or ESCALATE decisions with evidence, scope, audit hashes, and improvement signals.

Proposed ChangeOutput, action, write, update, signal, or handoff
→
OntoGuard CheckpointBM25 + semantic evidence, ontology grounding, triad, arbitration
→
DecisionALLOW, BLOCK, or ESCALATE before commit
→
Proof + LearningPacket, audit credential, human route, L3 signal
1. LLM / Model OutputProduction

Risk if ungoverned: unsupported answers, misleading recommendations, unsafe release, or false confidence.

OntoGuard produces: Decision API, evidence pack, triad, hallucination status, audit hashes, and review route.

2, 3, 4, 5 & 7. Agentic Extensions API Today · Enforcement Q3 2026

Current Status: The same Decision API and JSON contract used for LLM outputs is designed to support these use cases.

How it works: Agent frameworks can call OntoGuard’s Decision API before executing tool calls, memory writes, ontology changes, policy updates, or agent handoffs.

Expansion Roadmap: Full enforcement middleware and framework adapters (LangChain, CrewAI, AutoGen, etc.) are rolling out in Q3 2026.

6. Training / Alignment Signal ExportProduction

Risk if ungoverned: bad examples, unsafe corrections, or unreviewed behavior changes feeding future systems.

OntoGuard produces: gold-example candidates only when governed outcomes are approved, corrected, and traceable.

Production today: LLM output governance and L3 training signal export. Agentic extensions (tool calls, memory, ontology, handoffs) are available via API integration today, with full enforcement rolling out Q3 2026.

Ontology AI as the Semantic Layer for Enterprise AI

Enterprise systems already run on objects, relationships, and rules. OntoGuard turns that structure into Ontology AI: the Semantic Layer that lets AI reason over enterprise reality before a proposed state transition becomes a decision.

Enterprise Objects
  • Customer / Account / Claim
  • Policy / Contract / Case
  • Vendor / Asset / Location
  • Obligation / Exception / Evidence
Relationships & Rules
  • Customer → owns → Account
  • Claim → references → Policy
  • Contract → restricts → Data Use
  • Case → requires → Review Outcome

Semantic Layer

Maps prompts, outputs, policy scope, BM25 evidence, clause hits, enterprise objects, and symbolic traces into one governed representation.

Reasoning Layer

Turns semantic evidence into allowed, blocked, or escalated decisions with uncertainty, risk, arbitration, and human-review routing.

Cognition Layer

Feeds approved or corrected outcomes back into L3 training signals, policy improvements, retrieval improvements, and future governance quality.

Together, these layers form a System of Intelligence: not just model output, but enterprise-aware reasoning, authorization, evidence, and learning.
Enterprise Reality Objects, relationships, policies, obligations, evidence, and workflow state
+
AI Proposal Output, tool call, memory write, ontology update, policy mapping, training signal, or handoff
→
OntoGuard Semantic Layer Grounds, reasons, routes, audits, and converts decisions into governed learning signals
Result: “We can prove it — and here is the semantic path from enterprise state to governed decision.”

Governed State Transition Record

The buyer-readable PDF is one view of the proof. The Governed State Transition Record is the structured JSON contract underneath it. In the current v1.2.0 lineage, the record already carries the fields needed for audit, routing, repair, and improvement loops.

before_state
Prompt, workflow, scope, and current object context proposed_action
Output, tool call, memory write, mapping, or handoff evidence_state
Clause hits, retrieval IDs, checksums, symbolic trace triad_with_meta
Cold, Heat, Mercury, provenance, and penalties audit_credential
Decision, evidence, report, and manifest hashes improvement_signal
Review outcome and L3 gold-example candidate

L3 Training Signal Export — From Decision to Gold Example Candidate

The same governance packet that protects a workflow can also create clean, reviewable signals for better retrieval, policies, evaluation sets, and future agents.

AttemptModel, agent, or tool call proposes an output.
→
OntoGuard DecisionALLOW, BLOCK, or ESCALATE is computed before release.
→
Evidence + ReasonsTrace, clauses, uncertainty, risk, and reason codes are exported.
→
Gold ExamplesApproved review outcomes become candidate training and policy examples.
→
Better Decision MakingFuture retrieval, policies, agents, and evals improve from governed signals.

What Real Enterprise Output Looks Like

This is a buyer-safe financial services pilot output based on a governed enterprise workflow. OntoGuard mapped financial-services evidence, detected a remaining SEC citation-linkage gap, failed the strict autonomous-release benchmark gate, and withheld release pending human review — exactly the commercially valuable control-plane behavior regulated buyers need.

Financial Services Pilot Buyer-Safe Telemetry SEC Citation-Linkage Gap Detected Governed State Transition Record v1.2.0 Strict Release Gate Failed
DecisionESCALATE
Release ControlWITHHELD PENDING REVIEW
Primary ScopeSEC / SOX / GLBA / FINRA
CoverageNo coverage gaps
Citation GapSEC pending citation linkage
Trust81.41%
TriadCold 100.00% | Heat 6.00% | Mercury 96.67%
BenchmarkComparable corpus available; strict release gate did not pass
ROIHard-dollar ROI not calculated — buyer baseline required
Artifact Depth18-page audit PDF + 6-page sellable-lite packet + schema-rich JSON

ROI Status

Hard-dollar ROI not calculated — buyer baseline required.

To calculate ROI, provide:

  • monthly_case_volume
  • baseline_review_minutes_per_case
  • ontoguard_review_minutes_per_case
  • fully_loaded_reviewer_hourly_rate
  • baseline_false_approval_rate
  • ontoguard_false_approval_rate
  • average_loss_per_false_approval
  • implementation_cost_usd
  • assumptions_source

ROI Results

Monthly review savings—
Annual loss prevention—
Total annual value—
Payback months—
Assumptions source—
Why this example matters: OntoGuard did not approve the AI output just because the trust score was 81.41%. It detected a citation-linkage gap, failed the strict autonomous-release benchmark gate, and escalated the decision for human review.

That is the product: not a score, but a release decision with evidence, reasons, and review routing.
Request Audit PDF + Sellable-Lite Packet Request Schema-Rich JSON Download Financial Services Sample Packet
Similar artifacts have been delivered across KYC, claims, prior authorization, and benefits determination workflows. The financial-services sample packet is available for open website visitors. Additional workflow-specific packets can be shared in sanitized or NDA-gated form.

Current Capability vs. Expansion Roadmap

Core capabilities — LLM Output Governance and L3 Training Signals — are live in production today. The same governance engine and Decision API contract extend to agentic workflows through API integration.

Production Today

LLM Output Governance + L3 Training Signals

Full runtime governance layer, buyer-safe telemetry, audit hashes, human-review routing, and closed-loop training signal export.

API Today · Enforcement Q3 2026

Agentic State Governance

Tool call authorization, memory write control, ontology change proposals, policy mapping updates, and multi-agent handoff governance.

Available now via API integration. Full enforcement middleware and framework adapters are rolling out in Q3 2026.

💼 Enterprise Workflows We Govern Today

OntoGuard governs proposed AI state transitions in workflows where mistakes become cost, regulatory exposure, operational delay, or customer harm.

Financial Services

KYC and onboarding approvals, claims and dispute resolution, underwriting and credit decisions, advisor copilots, SEC / FINRA reporting support, fraud operations, and customer communications.

Healthcare

Prior authorization, eligibility checks, clinical documentation, coding support, patient triage, care navigation, medical chatbot review, and safety escalation.

Public Sector & Enterprise Ops

Benefits and eligibility determinations, casework, investigations, procurement approvals, customer operations, refunds, exceptions, IT automation, and change approvals.

Human Review Options: APPROVE REWRITE BLOCK

How OntoGuard Actually Works

OntoGuard exports the Semantic Layer and Reasoning Layer: ontology scope → BM25/semantic evidence → clause coverage → arbitration → audit hashes → human routing → L3 signal.

1 · Governed State Transition Record

v1.2.0-style structure carries before state, proposed action, evidence state, decision, triad metadata, audit credential, and improvement signal.

2 · BM25 + Semantic Candidate Bag

Current-run prompt, response, and scope anchors feed lexical BM25, semantic retrieval, clause normalization, checksums, and no-silent-drop telemetry.

3 · Primary-Scope Gap Penalty

Cold index is penalized when decision-driving regulations such as FINRA, GLBA, SEC, or SOX have zero coverage, even when supplemental regulations are mapped.

4 · Buyer-Safe vs. Internal Lanes

Buyers see clean decision evidence; internal lanes preserve repair flags, monotonic markers, schema fences, diagnostics, and repair provenance.

5 · Transparent Agent Consensus

Compliance, Accuracy, Risk, and Feedback agents expose votes, disagreement, consensus, and native arbitration computation.

6 · Portable Proof

Financial pilot artifacts expose 87-key JSON depth, hashes, coverage gaps, evidence anchors, schema-constrained fields, and L3 improvement readiness.

Differentiation: OntoGuard does not bury meaning in notebooks or pipeline glue. It exports the semantic chain as a buyer artifact.

Regulatory Strength as a Secondary Superpower

OntoGuard is broader than compliance tooling, but regulatory readiness remains a powerful entry point. The platform can expose whether the regulations that matter for a workflow are actually covered — not merely whether some evidence was found.

Financial Services

FINRA, GLBA, SEC, SOX, credit, advisory, customer communications, trading support, regulated reporting, and primary-scope coverage gaps.

Healthcare and Life Sciences

HIPAA, PHI handling, clinical support workflows, prior authorization, medical chatbot review, patient communication, and safety escalation.

Privacy and AI Governance

GDPR, EU AI Act readiness, privacy obligations, risk disclosures, human-review routing, and evidence-backed decision records.

Public Sector and Operations

Eligibility, procurement, exception handling, casework, audit trails, reviewer accountability, and policy-change governance.

We do not just check boxes. We surface coverage gaps in the laws, policies, and enterprise rules that actually drive the workflow.
Under the current EU AI Act timeline, key high-risk and transparency obligations become applicable around August 2, 2026, while simplification amendments remain in motion.

Governance Feature Remediation Expansion Roadmap

Every packet can expose a buyer-readable next human step tied to the exact L1, L2, or L3 capability that needs remediation.

L1 Evidence Remediation: add or verify missing evidence, citation, retrieval ID, clause mapping, or ontology scope.
L2 Consensus Remediation: resolve trust, risk, uncertainty, hallucination, or semantic disagreement before release.
L3 Feedback Remediation: convert reviewer outcome into policy, retrieval, evaluation, or gold-example improvement signal.

Why This Matters to the Buyer

Buyers do not need another opaque score. They need a defensible answer to four questions: should this AI change commit, why, what evidence proves it, and what happens next?

Commit?ALLOW, BLOCK, or ESCALATE
Why?Reason codes, triad, risk, uncertainty
Proof?Evidence IDs, retrieval IDs, hashes, trace
Next?Human route or L3 improvement signal

How It Works

OntoGuard uses a three-layer Semantic Governance Stack: L1 Symbolic Grounding, L2 Semantic Consensus, and L3 Alignment Feedback. The result is not just a score — it is a governed release decision with evidence, traceability, and reusable learning signals.

1. The AI Evolution

Diagram showing the evolution from basic LLM outputs to ontology-grounded runtime cognitive control and state-transition governance

2. Solving the Peak Data Problem

Diagram explaining the peak data problem and how ontology-grounded AI governance creates reusable evidence and learning signals

3. The AI Trust Pipeline

OntoGuard AI trust pipeline showing semantic governance, evidence retrieval, compliance scoring, and decision authorization

4. Executive Summary

One-page visual summary of OntoGuard runtime cognitive control plane, symbolic trace, evidence pack, triad scoring, and governed state transition record 📄 NDA Access Request

Not Just Ontology — Runtime Authorization + Learning Loop

  • 🧠 Decision API: ALLOW, BLOCK, or ESCALATE with reasons, confidence, trace ID, evidence, and release status
  • 🔁 Training Signal Export: governed decisions become clean examples for better future agents
  • 📊 Semantic Governance Triad: Cold, Heat, and Mercury explain grounding, volatility, and trace fidelity
  • 🛡️ Audit-Ready Proof: governed response, evidence, hashes, hallucination status, uncertainty, and human-review task

OntoGuard is protected by U.S. Patent Application 19/444,521 — Track I prioritized examination granted May 2026; the technology produces runtime authorization decisions backed by evidence, routing, auditability, and improvement signals.

Public sample packet available now. Full technical details, claims mapping, and private demo assets available under NDA.

Glossary for Runtime Cognitive Control Buyers

Ontology AIAI grounded in enterprise objects, relationships, policies, evidence, and symbolic traces rather than prompt text alone.
Semantic LayerThe enterprise meaning layer that maps prompts, outputs, scope, BM25 evidence, clause hits, objects, and rules into a governable representation.
Reasoning LayerThe layer that converts semantic evidence, uncertainty, risk, and arbitration into allowed, blocked, or escalated decisions.
Cognition LayerThe feedback layer that converts governed outcomes into L3 training signals, retrieval improvements, policy updates, and future decision quality.
System of IntelligenceThe full operating model: semantic grounding, reasoning, runtime authorization, auditability, human review, and learning signals.
Runtime Cognitive Control PlaneThe backend layer that authorizes, blocks, escalates, records, and improves high-stakes AI state transitions.
State-Transition GovernanceGovernance applied to proposed AI changes. Currently in production for LLM outputs and L3 training signals. Support for tool calls, memory writes, ontology changes, policy updates, and multi-agent handoffs is available via API integration and in active development for full enforcement.
Governed State Transition RecordA portable PDF + JSON receipt proving how an AI proposal was governed before release.
Enterprise OntologyObject-oriented enterprise reality: customers, accounts, claims, contracts, policies, relationships, obligations, and evidence.
Buyer-Safe TelemetryClean decision-relevant data separated from internal repair flags, hardening markers, and diagnostic lanes.
L3 Training Signal ExportGoverned outcomes transformed into reviewable improvement examples for retrieval, policies, alignment, and evals.

FAQ

What is state-transition governance?

State-transition governance asks whether a proposed AI change is authorized, traceable, and safe to commit. The proposal may be an LLM output, tool call, memory write, ontology update, policy mapping, training signal, or multi-agent handoff.

Do you support tool calls, memory writes, and multi-agent handoffs today?

Not at the same production level as LLM output governance yet. The core Decision API and JSON contract are designed to support these use cases through integration. Agent frameworks can call OntoGuard before executing actions. Full enforcement middleware and adapters for LangChain, CrewAI, and other frameworks are currently in development.

What is a Decision Authorization Packet?

A Decision Authorization Packet is a portable PDF and JSON proof artifact showing how a high-stakes AI output was authorized, escalated, or blocked before release. It records the ALLOW, BLOCK, or ESCALATE decision, evidence, reasons, uncertainty, hallucination status, symbolic trace, human-review routing, audit hashes, and improvement signals.

Is OntoGuard only a regulatory compliance tool?

No. Regulatory readiness is a strong wedge, but OntoGuard is positioned as a runtime cognitive control plane for governing proposed state transitions across enterprise workflows, agentic systems, memory, tools, ontology, and training feedback.

How does OntoGuard use Ontology AI as a Semantic Layer?

Ontology-grounded objects, relationships, rules, clause hits, domain scope, evidence, and symbolic traces connect AI proposals to enterprise reality. This Semantic Layer becomes the Reasoning Layer and Cognition Layer that lets OntoGuard prove why a state transition was allowed, blocked, or escalated.

Which state transitions can OntoGuard govern?

The current production center is LLM output governance and L3 training-signal export. The same packet contract extends through integration to agent tool calls, memory writes, ontology changes, policy mappings, and multi-agent handoffs, with full enforcement middleware and framework adapters in active development.

What makes the financial services pilot important?

It shows OntoGuard doing the commercially valuable thing: withholding release despite an 81.41% trust score because a SEC citation-linkage gap remained and the strict autonomous-release benchmark gate did not pass.

What happens when evidence is incomplete?

The packet still exports. OntoGuard routes uncertainty to SAFE_TEMPLATE or HUMAN_REVIEW, records reason codes, preserves audit hashes, flags coverage gaps, and avoids silently blank evidence or failed artifacts.

Request an AI Output Risk Scan

Send 50–500 prompts, responses, API logs, or demo outputs. OntoGuard will return a behavior risk map, review-burden analysis, autonomy-readiness tiers, training-signal candidates, and sample proof packets for high-risk outputs.

No internal document upload, production deployment, model retraining, model-weight changes, or workflow interruption is required to start.

Run an AI Output Risk Scan Get a Demo Proof Pack Reduce Human Review Burden Download Sample Proof Packet Contact for NDA

For deeper diligence, we can provide NDA terms, private packet artifacts, integration materials, and technical briefings.

Contact

Email mark.starobinsky@ontoguard.ai for pilots, licensing, partnerships, or NDA access.