Platform
The AI Verification Engine
Six verification layers that sit between your AI systems and your users. Not monitoring. Not observability. Verification—every output tested, every claim traced, every deployment proven.
Architecture
How verification works
Every AI output passes through a six-layer pipeline before reaching production. Each layer operates independently. No single point of failure.
Dual-View Verification
Every output goes through an independent verification process before delivery. Disagreements are resolved with documented reasoning.
Not self-reflection. Not "check your work." A structurally separate review designed so the verification is genuinely independent from the generation. When the verification process finds a problem, the output's confidence score is directly reduced.
How it works
AI output is generated normally using your existing models
An independent verification process challenges the output for errors, unsupported claims, and logical flaws
Disagreements are resolved with documented reasoning and confidence scoring
Output delivered with per-claim verification status and overall confidence score
Detection categories
Confidence Inflation
AI becomes more certain than evidence supports
Data Fabrication
Inventing data when under pressure
False Expertise
Claiming authority beyond actual capability
Qualifier Loss
Hedging language stripped during processing
Premature Completion
Declaring done before full verification
Error Propagation
Failures spreading between AI agents
Behavioral Pattern Detection
A comprehensive library of documented AI failure modes. Each discovered through controlled experiments across production AI models.
AI systems fail in predictable, detectable ways. They become more confident when praised. They fabricate data under pressure. They mimic authority they don't have. We detect these patterns before the output reaches your users.
Claim-Level Verification
Every factual claim extracted, source-traced, and tagged. When an AI cites a legal case, we verify it exists and says what the AI claims.
Designed to prevent failures like the hallucinated citations in Mata v. Avianca before they reach the court. Each claim receives a verification status: VERIFIED, UNVERIFIED, or CONTRADICTED -- with source documentation.
Example output
"Revenue reached $4.2M"—matches SEC 10-Q filing
"23% increase"—no source document supports this figure
"Enterprise contract expansion"—consistent with earnings call
Oversight tiers
Tier 1: Autonomous
Read operations, data retrieval, formatting
Tier 2: Monitored
Analysis, recommendations, report generation
Tier 3: Supervised
Financial calculations, medical summaries, legal research
Tier 4: Human Required
Clinical decisions, financial transactions, legal filings
Pre-Execution Oversight
Before any AI agent acts, the action is classified into oversight tiers. Read operations proceed autonomously. Clinical decisions require human approval.
Actions are classified before execution, not after. The AI cannot decide its own oversight level. Classification considers the stakes and context of each action.
Memory Quarantine
AI memory is treated as untrusted until verified. Untrusted memory is isolated and subject to verification requirements before it can influence future outputs.
When an AI system remembers something from a previous session, how do you know that memory is accurate? Memory Quarantine isolates unverified data, prevents stale information from influencing new outputs, and detects temporal inconsistencies.
Memory states
Human-confirmed data, external source validated
AI-generated analysis, unverified by human
Contradicts verified data, isolated from active use
Zero-trust enforcement
Every agent-to-agent message treated as potentially compromised
Permission scoping per agent capability
No agent can rewrite its own governance constraints
Error propagation caught at every agent boundary
Zero-Trust Multi-Agent Governance
When Agent A hallucinates and passes the output to Agent B, by Agent D the hallucination is treated as verified fact. We stop this at every boundary.
Every agent-to-agent communication is verified. No agent trusts another agent's output without independent verification. Error cascade propagation is impossible by architecture, not by policy.
Cross-Cutting Capabilities
Beyond the Six Layers
Platform-wide capabilities that operate across every layer simultaneously.
Hardware Independence
Substrate-Agnostic Governance
Works across classical, neuromorphic, photonic, quantum-classical hybrid, and 4 more compute substrates. One governance framework, any hardware. As compute architectures evolve, the governance layer does not need to be rebuilt from scratch.
Integrity Monitoring
Self-Healing Governance
The governance system monitors its own integrity. Integrity tests detect when evaluators start rubber-stamping. Automatic recalibration when evaluator drift is detected. The system that verifies AI outputs cannot itself become complacent without triggering a correction.
Adaptive Depth
Adaptive Governance Scaling
As AI capabilities accelerate, governance depth automatically scales. Prevents the gap between AI capability and governance coverage from widening. Designed for a world where the models deployed today are not the models deployed next quarter.
Auditor-Ready Proofs
Zero-Knowledge Compliance
Prove governance compliance to auditors without revealing the underlying data. Demonstrate that a process was followed correctly without exposing proprietary inputs, patient records, or confidential business data.
See the verification engine in action.
Schedule a technical deep-dive with our team. We'll run your actual AI outputs through the six-layer pipeline.