A Different Architecture
Most compliance platforms add AI to existing software. We built a governance engine where AI is the foundation.
Traditional
Rule-based engines built in the 2000s. Process transactions in batches. Flag based on static thresholds. Manual investigation.
AI-Augmented
Machine learning models added to existing platforms. Better pattern detection, but the same batch architecture underneath. AI assists humans.
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. | 500K entities under 2 seconds on the fixed synthetic fixture, with about 55K transactions. This is a detection test, not the full product cycle. |
| 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. | The prototype supports bounded information sharing and signed decision records under the stated trust model. |
| Processing Speed | Batch. Overnight or hourly. Thousands of decisions per second. | Near-real-time. Minutes to hours. Tens of thousands per second. | 500K entities under 2 seconds on the fixed synthetic fixture, with about 55K transactions. This is a detection test, not the full product cycle. |
| Governance Model | Configuration files. Change management committees. Months to update a rule. | Model governance. Bias testing, validation reports. Weeks to retrain and deploy. | The prototype supports bounded information sharing and signed decision records under the stated trust model. |
| Audit Trail | Log files. Database records. Manually reviewed during examinations. | Log files plus model cards. Feature importance scores. | The prototype supports bounded information sharing and signed decision records under the stated trust model. |
| Explainability | Rule X matched. Human analyst explains the context. | SHAP values. Feature importance. Partial dependence plots. Requires data science expertise to interpret. | The prototype supports bounded information sharing and signed decision records under the stated trust model. |
| 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. | 500K entities under 2 seconds on the fixed synthetic fixture, with about 55K transactions. This is a detection test, not the full product cycle. |
| 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. | The prototype supports bounded information sharing and signed decision records under the stated trust model. |
The Numbers
Entities under 2 seconds in a fixed synthetic test, about 55K transactions. Detection test only.
Measured detection-pass working set only. Synthetic data. Resident ingestion and model inference excluded.
The prototype supports bounded information sharing and signed decision records under the stated trust model.
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.