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
Two independent AI models with opposing mandates. Track 1 generates. Track 2 challenges. Synthesis resolves disagreements with documented reasoning.
Not self-reflection. Not "check your work." Genuine adversarial review between separate systems that cannot access each other's internal state. When Track 2 finds a problem, Track 1's confidence score is directly reduced.
How it works
Track 1 generates output with full context and instructions
Track 2 receives same prompt with adversarial mandate: find errors, challenge claims, test reasoning
Synthesis layer resolves conflicts, assigns confidence scores, documents reasoning chain
Output delivered with per-claim verification status and overall confidence score
Detection categories
Sycophancy Escalation
Agreement without re-evaluation
Fabrication Under Pressure
Inventing data when uncertain
Authority Mimicry
Performing expertise not held
Hedge Evaporation
Qualifiers lost in processing
Completion Bias
Declaring done before verified
Cascade Propagation
Errors spreading between agents
Behavioral Pattern Detection
150 documented failure modes across 14 categories. Each discovered through controlled experiments across 30+ 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.
The system that caught the hallucinated citations in Mata v. Avianca before they reached 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.
The classification happens before execution, not after. An AI agent cannot decide its own oversight level. The system classifies based on action type, domain, reversibility, and potential impact.
Memory Quarantine
AI memory is treated as untrusted until verified. Epistemic tagging, temporal decay, and quarantine zones prevent memory poisoning.
When an AI system remembers something from a previous session, how do you know that memory is accurate? Memory Quarantine assigns confidence decay over time, quarantines unverified memories, and prevents temporal hallucination.
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
Cryptographic 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 drawn from the patent portfolio 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. Canary 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
Governance Velocity Matching
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. Cryptographic proofs of regulatory predicate satisfaction. 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.