From static records to a living enterprise model.
Most organisations hold their enterprise in lists. Regurai holds it as a structure: thirteen layers, connected, so that a change in one is visible in all the others.
Select a layer to see what it represents and what it changes.
Relationships. Dependencies. Consequences.
AI Systems & Models
- What it represents
- AI systems, models and agents, with owners, purpose and lifecycle state.
- What it connects to
- Data, applications, processes, risks, controls and obligations.
- Decisions it supports
- Build, buy, deploy, restrict, retrain or retire.
- Risks it exposes
- Model risk, drift, excessive autonomy and regulatory exposure.
- Downstream consequence
- A model change re-scores connected risk, control and value views.
How the model appears in the platform.
The thirteen layers are the conceptual structure of the enterprise model — not a list of screens. The application groups that model into seven working areas, each reading and writing the same structure.
Command
Leadership sees the enterprise as it is today, with exceptions surfaced rather than discovered later.
- Dashboard
- Digital Twin
- AI Workforce
- AI Agent Observability
- Exception Center
More detailLess detail
Presents the live operating picture of the enterprise, its AI workforce and anything currently off-track.
Executives need one place that shows the current position rather than a reporting pack assembled after the fact.
Enterprise Architecture
Change is planned against real dependencies, so consequences are known before commitment.
- EA Command Center
- Capability Map
- Application & Technology
- Data Architecture
- Dependency Analysis
- Portfolio Heatmap
- EA Copilot
More detailLess detail
Holds capabilities, applications, technology, data architecture and the dependencies between them.
AI, risk and regulatory exposure cannot be judged without knowing what the estate is and what depends on what.
Data Governance
AI decisions rest on data whose origin, quality and permitted use are known.
- DG Command Center
- Data Catalog
- Business Glossary
- Data Quality
- Governance Exceptions
- DCAM Maturity
- Governance Automation
More detailLess detail
Governs the data behind the AI — ownership, meaning, quality, sensitivity and maturity.
Most AI risk is data risk. A quality or provenance problem is an AI exposure, not a data ticket.
EU / UK Financial Governance
Regulatory positions are supported by mapped controls and retrievable evidence.
- EU/UK Model Risk Management
- Operational Resilience
- Consumer Duty & Fairness
- Financial Crime
- Compliance Hub
- Controls Library
- Framework Coverage
- Evidence Vault
More detailLess detail
Supports model risk management, operational resilience, consumer fairness, financial crime, controls and evidence.
Regulated firms must be able to demonstrate a governed position, not describe an intention.
AI Governance
Every AI system in use has an owner, an assessed risk position and a recorded decision to run it.
- Model Inventory
- AGEE
- Risk & Impact
- Approvals
More detailLess detail
Registers AI models, agents and AI-enabled applications, assesses their risk and records approval.
AI adoption moves faster than governance unless registration, assessment and approval sit in one place.
Analytics & Tooling
Boards, auditors and regulators receive evidence from the same model the business runs on.
- Audit Explorer
- Audit Log
- Simulation Lab
- Risk Center
- Reports
- Audit Log Viewer
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Provides audit exploration, simulation, risk analysis, reporting and integration.
Governance is only credible when it can be examined, tested and reported without manual reconstruction.
Learn
Teams adopt the platform without depending on tribal knowledge.
- Help Centre
More detailLess detail
Offers in-product guidance covering each module, its purpose and how it is operated.
Governance capability depends on the people using it, not only the platform holding it.