The Regitech Solution
A modular, provider-agnostic approach to AI accountability: capture the provenance of a reasoning process as it happens, so the decision can be explained, reviewed and challenged afterwards. Our research programme spans Chain of Thought, Graph of Thought, JEPA world models and agentic swarms.
The Governance Complexity Paradox
As AI systems evolve from Chain of Thought reasoning through Graph of Thought architectures to JEPA world models and autonomous agentic swarms, they become exponentially harder to monitor and govern effectively.
Why Traditional Compliance Fails:
- •Post-Processing Limitation: Adding disclosure metadata after content generation cannot capture dynamic reasoning paths
- •Temporal Disconnection: Retrospective disclosure injection misses real-time reasoning changes
- •Verification Impossibility: No cryptographic chain linking reasoning process to final output
- •Opacity Crisis: Advanced models perform reasoning in latent, unobservable spaces
Our Patent-Pending Innovation: Latency-Optimized Reasoning Provenance Injection
Regitech monitors AI reasoning at the token-generation level and injects latent disclosures during the reasoning process — not after. Our design target is sub-millisecond added latency; performance figures on this page are engineering targets under validation, not benchmarked production results.
Layer 1: Blockchain Provenance System
An optional, multi-tier anchoring layer that can combine public-ledger transparency with private-ledger performance to make an evidence record tamper-evident. Anchoring is optional and subordinate to the evidence itself — the audit trail works without it.
- SHA-256 cryptography
- Multi-chain consensus
- Tamper-evident records
- Hyperledger Fabric for confidential data
Layer 2: Multi-Agent Monitoring
A durable, graph-based agent design (such as LangGraph) for reliable execution. Target detection performance is >95% accuracy with >85% obfuscation identification; these are development targets currently in validation and are not yet independently benchmarked.
- Detection agents (>95% accuracy — target, in validation)
- Analysis agents identify obfuscation (>85%)
- Compliance agents enforce regulations
- Dispute processing with NLP classification
Layer 3: Latency-Optimized Injection
Design targets of sub-millisecond compliance embedding (<1ms for Chain-of-Thought, <5ms for diffusion models) and 50–80% token efficiency for JEPA architectures. These are engineering targets from internal work, not validated production benchmarks.
- <1ms latency for token-based models
- <5ms latency for diffusion models
- 50–80% token efficiency (embedding-space)
- Parallel processing with predictive caching
Layer 4: Integrated Dispute Resolution
Three-tier escalation framework (70% resolution, 20% mediation, 10% arbitration) providing constitutional due process.
- Automated smart contract resolution (70%)
- AI-assisted mediation with human oversight (20%)
- Binding arbitration
- Multi-signature oracle consensus
Advanced Reasoning Model Support
Designed to capture evidence at the point the decision is made, across every generation of AI reasoning architecture—from sequential token prediction to embedding-space prediction.
Chain of Thought (CoT)
Linear reasoning path monitoring with step-by-step disclosure injection and fragility detection for obfuscated reasoning attempts.
Tree of Thought (ToT)
Multi-path validation monitors all parallel reasoning branches simultaneously with cryptographic linking of branch decisions to final output.
Graph of Thought (GoT)
Vertex-edge analysis monitors hundreds of interconnected thought vertices with feedback loop detection and emergent behavior monitoring.
JEPA & World Models
Embedding-space governance for architectures that reason in latent representations rather than observable token sequences—where post-processing tools have zero visibility.
Why Regitech Is Unique
Technical advantages that create an insurmountable competitive moat.
Sub-Millisecond Performance
Parallel processing and predictive caching are used to keep the overhead on AI reasoning performance negligible. Token-efficiency figures for embedding-space architectures are internal targets under validation.
Tamper-Evident Audit Trails
Evidence packages are structured so that any later alteration is detectable, and so that a regulator, an internal reviewer or the person affected can read the same record. Whether a given package satisfies a specific jurisdiction is a legal determination, not a technical one.
Architecture-Agnostic Monitoring
From sequential token reasoning to latent embedding-space prediction, Regitech governs at the reasoning level—not the output level—making it effective regardless of architecture.
Agentic Swarm Oversight
As enterprises deploy autonomous agent fleets with $80B+ in committed infrastructure, Regitech provides attribution chains and collective decision audit for long-duration autonomy.