Verifiable AI Control Plane
Deterministic AI.No Margin for Error.
Ontic Labs provides the verifiable control plane for high-stakes intelligence operators. No evidence, no emission.
Operational Reality
The black box fails under pressure.
When iteration is not a strategy, probabilistic models are a liability. The biggest models in the world are already fabricating case law, hallucinating intelligence, and leaking sensitive data in environments that cannot absorb uncertainty.
Curated from the AI Incident Database and Ontic operational baselines for high-stakes systems.
1,425+
Documented AI Failures
36%
Impacting Vulnerable Populations
Zero
Margin for Error in High-Stakes Operations
Manifesto
The Illusion of Parameter Scaling.
Scaling an LLM to fix hallucinations or leakage is the equivalent of adding rounds to a structurally weak block cipher. You are fighting asymptotic deceleration: a mathematical reality that does not disappear when you buy more parameters or extend the model's context window.
As the security margin vanishes, failure becomes inevitable. Certainty does not require a trillion parameters. It requires a control plane. Chat with our Inference Engine for proof.
Deployment Tiers
One Platform. Three Trust Boundaries.
Deploy deterministic intelligence wherever your mission requires it, from governed public-model overlays to chemically sealed sovereign infrastructure.
Workshop /01
Refinery /02
Clean Room /03
Sectors
Built for High-Stakes Operators.
Where a probabilistic hallucination is a catastrophic liability.
Defense & Intelligence
Prevents the leakage of compartmented intelligence to public weights and enforces verifiable AI inside disconnected, mission-bound environments.
Government & Public Sector
Constrains public-sector AI with policy-bound emissions, governed retrieval, and inspector-ready logs for legally accountable decision paths.
Healthcare & Life Sciences
Protects against PHI exposure and diagnostic hallucinations with sovereign infrastructure, evidence-bound outputs, and replay-grade auditability.
Regulated Enterprise
Prevents IP loss and regulatory breaches by wrapping enterprise models in a deterministic governance overlay before output reaches operations.
Ontic Control Plane
Powered by the Ontic Control Plane.
The proprietary enforcement mechanisms underneath every deployment tier, turning retrieval, synthesis, and emission into an auditable system rather than a probabilistic guess.
Foundry
The secure ingestion and provenance vault. Every downstream module inherits its source authority from Foundry or does not execute.
Query GovernanceCompiler
Rewrites and constrains every query before it reaches the model, enforcing policy and evidence requirements before synthesis begins.
Scoped RetrievalCrosswalk
Real-time, identity-aware retrieval via Zanzibar graph relationships so the model only sees what the operator is cleared to see.
Emission ControlLedger
Pre-emission entailment checks measure whether a generated claim can actually be proven by authorized source data. Unsupported claims are blocked.
Inference Engine
Our localized reasoning unit synthesizes intelligence inside the trust boundary without depending on the public internet or external model APIs.
Boundary Gate
A hard separation layer that keeps model priors distinct from sensitive RAG context and acts as the final circuit breaker before emission.
Resources
Technical articles, architecture context, and incident evidence for teams evaluating the Ontic control plane after the deployment and enforcement story is clear.
Reference
Documentation
Operator guides, implementation notes, and the current system surface for teams evaluating or integrating Ontic.
Platform Architecture
Review the topology, trust boundaries, and enforcement model behind the Ontic control plane.
AI Incident Archive
Explore real-world AI failures, harmed populations, and evidence patterns through the incident archive.