Enterprise AI Verification
Verification
Infrastructure
for High-Stakes AI
The verification engine that sits between your AI and your users. Built for industries where wrong answers have consequences.
About Ulfberht
AI verification that’s generations ahead.
The AI safety industry monitors outputs and reports problems after the fact. We built the verification engine that intercepts failures before they reach your users—with the deepest behavioral research in the industry.
Named after the Viking Ulfberht swords—forged with crucible steel 800 years ahead of their era. The original mark of engineering that could not be replicated.
Pre-deployment verification
Six independent verification layers inspect every AI output before it reaches production. Verified or blocked—nothing unproven gets through.
Behavioral failure detection
The largest documented catalogue of AI behavioral failures—tested across production models. Hallucination, fabrication, false expertise, and more.
Claim-level tracing
Every factual statement in an AI output is extracted, traced to an authoritative source, and marked verified, unverified, or contradicted. Evidence, not confidence scores.
Designed for regulated industries
The Problem
AI is deployed in critical systems without verification.
Healthcare diagnoses. Legal citations. Financial projections. Government intelligence. Every day, AI outputs reach production unchecked. When they're wrong, the consequences are regulatory, financial, and clinical.
100% error propagation in multi-agent systems
Agent A hallucinates. By Agent D, the hallucination is treated as fact.
Self-review catches 0% of structural failures
AI systems miss the same errors they generated. Under pressure, they fabricate confirmations.
Fabrication increases dramatically under evaluation pressure
AI told its output will be judged produces structurally indistinguishable fake data.
Verification Architecture
Six layers, working in sequence.
Not a monitoring dashboard. A verification engine where every AI output passes through six independent checks before it reaches production.
Dual-View Verification
Every output goes through an independent verification process before delivery. Disagreements are resolved with documented reasoning and per-claim confidence scores. Not self-reflection—genuinely independent review.
Method
Independent Verification
Output
Confidence score + audit
Behavioral Pattern Detection
A comprehensive library of documented AI behavioral failures, tested across production models from major providers. Each pattern has a documented detection method built into the pipeline.
Patterns
Comprehensive library
Coverage
Production AI models
Claim-Level Verification
Every factual claim in an AI output is individually extracted and traced to an authoritative source. Each claim is tagged as verified, unverified, or contradicted—with documentation, not confidence scores.
Method
Per-claim extraction
Output
Source-traced audit trail
Pre-Execution Oversight
Every AI action is classified into oversight tiers before execution. High-stakes actions—clinical recommendations, financial transactions, legal filings—require explicit human approval. No autonomous action in critical domains.
Method
Task classification tiers
Gate
Human-in-the-loop
Memory Quarantine
AI memory is treated as untrusted input. Every stored fact is verified before it can influence future outputs. Stale and unverified data is isolated automatically. No tainted memory chains.
Method
Memory integrity checks
Scope
All persistent state
Multi-Agent Governance
Zero-trust communication between AI agents. No agent can rewrite its own constraints or another agent's outputs. Error cascade prevention ensures a single compromised agent cannot propagate failures through the system.
Method
Zero-trust protocol
Protection
Cascade prevention
How It Works
AI generates. Ulfberht verifies.
Solutions
One verification engine. Industry-specific deployments.
Each vertical receives its own compliance module, failure mode library, and regulatory reporting format.
Clinical AI Verification
Diagnostic overconfidence detection. Drug interaction hallucination prevention. HIPAA-compliant audit trails.
Legal AI Verification
Citation verification against actual case law. Precedent fabrication detection. Sanctions prevention.
Financial AI Verification
Market data hallucination detection. Projection confidence scoring. SR 11-7 compliance.
Government AI Verification
NIST AI RMF compliance. Air-gapped deployment. Full audit trail export for oversight review.
Physical AI Verification
Action irreversibility classification. Sensor hallucination detection. Multi-robot cascade prevention.
Automotive AI Verification
ADAS decision verification. Perception system hallucination detection. Safety-critical classification.
Quantum & Advanced Computing AI Verification
Substrate-agnostic governance across classical, neuromorphic, photonic, and quantum-classical hybrid architectures.
Research
Built on evidence. Not marketing.
Every capability claim is backed by documented experiments, tested across multiple production AI systems.
Failure Modes
Comprehensive library
Testing
Extensive experiments
Coverage
Multi-provider models
Verification
6 independent layers
Key Finding
100% error propagation in ungoverned AI swarms.
When Agent A hallucinates and passes output to Agent B, by Agent D the hallucination is treated as verified fact.
Key Finding
Self-review catches 0% of structural failures.
AI reviewing its own output misses the same errors it generated. Only structurally independent verification works.
Get Started
Enterprise access by application.
Ulfberht is designed for organizations in regulated industries where AI errors carry regulatory, financial, or clinical liability.