Drop-in AI transparency & routing (EU AI-Act ready)
EU AI Act Article 12 Ready • NIST AI RMF Aligned
Fingerprints every decision, creates exportable audit logs for Article-12, and routes tokens to the healthiest model — all without modifying customer model files.
No model file modification • API-first architecture • Enterprise-ready compliance
Advanced Intelligence Monitoring Challenge
Enterprises need mathematical verification of AI intelligence patterns for regulatory compliance, risk assessment, and advanced capability detection
Compliance Requirements
EU AI Act and regulatory frameworks require auditable AI decision pathways. Post-hoc explanations aren't legally sufficient.
Operational Risk
Black box AI decisions create unlimited liability exposure and stakeholder confidence issues for enterprise deployments.
Board Accountability
Executives need to explain AI decisions to boards, regulators, and customers with mathematical precision, not statistical approximations.
HIGL • QGT • IGBP Framework Integration
Our proprietary math frameworks (HIGL, QGT, IGBP) power the health signals and routing. See Research for details.
How It Works
Mode A — Observe & Prove
Measure token-by-token health + signed audit trail for Article 12/NIST. No weights.
Mode B — Route & Optimize
Steer tokens to the healthiest provider/expert to raise quality and lower cost.
Free Tier Demo
Interactive Demo: Contact us for enterprise access to our AI intelligence monitoring platform.
AI Intelligence Monitoring Database
Proprietary mathematical frameworks for AI intelligence analysis. Contact us for enterprise access to our database of tested models with QGT coherence scores and geometric analysis capabilities.
Enterprise Model Database
Searchable database of 12+ tested models with safety scores, recommendations, and use case guidance
QGT Geometric Analysis
Quantum-Inspired Geometric Tensor processing provides mathematical verification of AI capabilities on standard hardware
HIGL Framework Integration
Hilbertian Information Geometry analysis for comprehensive AI intelligence monitoring
Advanced Mathematical Frameworks
Our proprietary mathematical foundations enable unprecedented insight into AI decision-making and geometric intelligence patterns for enterprise-grade transparency.
HIGL Framework
Hilbertian Information Geometry of Learning
Unifies Hilbert space semantics with information geometry for rigorous neural network analysis.
- • Typed Hilbert construction for neural computation
- • Quantum-inspired differential geometry
- • Advanced computational algorithms for efficient analysis
Multi-Signal Analysis
Advanced Pattern Recognition Processing
Analyzes decision patterns through capacity measurement and behavioral dynamics tracking.
- • Capacity signals: Model expressivity and representation power
- • Dynamic signals: Path-dependent AI decision behavior
- • Standard GPU hardware deployment
Stability Balance
Learning Stability Monitoring
Ensures information growth maintains optimal balance for stable model learning.
- • Real-time stability condition monitoring
- • 6 entropy measures for comprehensive analysis
- • Validated across 5 architecture families
Architecture Validation
Comprehensive testing across five neural architecture families with classical and holographic variants
A4-GNN
Tetrahedral equivariant graph neural networks
Fourier
Frequency-domain holographic processing
GNN
Graph neural network implementations
HAM
Holographic associative memory
VAE
Variational autoencoder architectures
Ready for Mathematical AI Transparency?
Discover how HIGL, QGT, and IGBP frameworks enable enterprise-grade AI intelligence monitoring
Enterprise partnerships • Direct collaboration