Technology Comparison
A Different Architecture
Most compliance platforms add AI to existing software. We built a governance engine where AI is the foundation.
2000s Architecture
Traditional
Rule-based engines built in the 2000s. Process transactions in batches. Flag based on static thresholds. Manual investigation.
Bolt-on AI
AI-Augmented
Machine learning models added to existing platforms. Better pattern detection, but the same batch architecture underneath. AI assists humans.
ZQUAS
AI-Native
Built from the ground up as a GPU-native runtime. Each monitored entity carries its own state, risk profile, and decision history, and is evaluated against the policy set on every change. AI sits inside the same governance perimeter as the rules engine, not bolted on after the fact. Decisions are computed, verified, and signed at GPU speed.
| Category | Traditional | AI-Augmented | ZQUAS |
|---|---|---|---|
| Architecture | Database and rules engine. SQL queries on stored transactions. | Database with ML models. Batch scoring added to existing pipeline. | GPU-native entity system. Each monitored account carries its own state, risk profile, and decision history, evaluated against the policy set on every change. 150 million+ policy evaluations per second. Alert lifecycle under 10ms. |
| AI Integration | None. Rules are manually authored and maintained. | ML models flag transactions for human review. Models are trained offline, deployed as scoring services. | AI is the runtime. Every entity operates as an agent that perceives, evaluates, and acts within a governed framework. Agents propose actions. A governance validation layer verifies every proposal against certified rules before execution. |
| Cross-Institutional Detection | Not possible. Each institution operates in isolation. | Not possible. Same data silo, better algorithms. | Privacy-preserving federation: ECDH-PSI + Garbled Circuits + OT. Banks jointly detect suspicious entities without sharing data. ~15 seconds per bilateral round at 100K entities. Dual Ed25519 attestation per round. |
| Processing Speed | Batch. Overnight or hourly. Thousands of decisions per second. | Near-real-time. Minutes to hours. Tens of thousands per second. | Sub-10ms alert lifecycle. 500,000 entities evaluated in under 2 seconds on a single GPU. 150M+ policy evaluations per second. |
| Governance Model | Configuration files. Change management committees. Months to update a rule. | Model governance. Bias testing, validation reports. Weeks to retrain and deploy. | Three-layer governance. Layer 1: formally verified execution core (immutable at runtime). Layer 2: certified rule schemas (versioned, validated). Layer 3: AI-generated content (every decision audited with reasoning). A governance validation layer sits between the rules and the AI, rejecting any proposal that violates the certified schema. |
| Audit Trail | Log files. Database records. Manually reviewed during examinations. | Log files plus model cards. Feature importance scores. | Cryptographic audit ledger. Every governance decision is recorded in a tamper-proof, cryptographically chained log. Ed25519 signatures. Blake3 integrity hashing. The audit trail is mathematically verifiable, not just reviewable. |
| Explainability | Rule X matched. Human analyst explains the context. | SHAP values. Feature importance. Partial dependence plots. Requires data science expertise to interpret. | Glass Box. Every decision carries a structured explanation of why it was made, what alternatives were considered, and which rules applied. Readable by compliance officers, not just data scientists. |
| Temporal Control | Replay from database snapshots. Hours to reconstruct a scenario. | Limited replay. Model inference on historical data. | Full temporal control. Pause any entity. Step forward one decision at a time. Rewind to any point. Launch parallel scenarios to test what-if hypotheses on live state. Inject simulated threats and measure system response. |
| Spatial Awareness | None. Transactions are flat records. | Graph analytics on stored relationships. | Volumetric spatial perception. Each entity has ambient awareness of risk, activity, and density in its neighbourhood of the entity graph. Entities surrounded by high-risk neighbours behave differently. Emergent intelligence, not just pattern matching. |
| Scalability | Add servers. Add databases. Linear cost scaling. | Add GPU instances for model inference. Separate from core platform. | Single GPU: 500K entities, 100 policies in under 2 seconds. Under 1GB VRAM for the full pipeline. Headroom for larger populations and concurrent federation. |
| Multi-Industry | Financial services only. Separate products for each regulation. | Financial services only. Domain-specific models. | Industry-agnostic governance engine. Financial crime is the first application. The same engine governs any domain where autonomous entities must operate under verifiable rules. |
| Privacy | Data centralisation. Access controls. Pseudonymisation. | Same as traditional, plus model access controls. | Multi-party computation. Zero raw data exposure between institutions. Mathematical privacy guarantees endorsed by the European Data Protection Board. |
Measured, Not Projected
The Numbers
150M+
compliance policy evaluations per second
<10ms
alert lifecycle, ingestion to triage
<2s
500K entities, 100 policies
12,342
automated tests
All numbers measured on NVIDIA RTX 5090. Medians of 3 runs, 10 warmup iterations. Every result signed.
The difference between adding AI to compliance software and building compliance on AI is architectural. It cannot be retrofitted. It must be built from the ground up.