The intelligence layer connecting AI to the enterprise.
Regurai is an AI intelligence platform. It gives organisations a connected intelligence layer for understanding how AI interacts with the enterprise — connecting AI to business, strategy, processes, applications, data, technology, risk, controls, regulation, value, decisions and evidence.
Seven kinds of intelligence, one connected model.
Each domain answers an executive question first; the underlying capability sits beneath it.
Understand what the organisation actually runs on, and what depends on what.
Enterprise architecture, capability mapping, application and technology portfolio, dependency analysis.
See where AI operates, what it is doing and where intervention may be required.
AI and model inventory, AI workforce, agent observability, AI-enabled applications.
Know the data behind AI decisions — its meaning, quality, ownership and permitted use.
Data catalogue, business glossary, data quality, lineage, data architecture.
Understand what could go wrong, what would be affected and how severe it would be.
Risk and impact assessment, resilience, exceptions, portfolio heatmaps, scenario simulation.
Make decisions with the business, economic and operational context attached.
Decision workspaces, AGEE economics, simulation, reporting and analytics.
Apply controls, approvals and accountability where they are needed.
AI governance, data governance, financial governance, controls library, approvals.
Show what happened, who decided it and on what basis.
Audit explorer, audit log, evidence vault, framework coverage, reporting.
Thirteen connected layers.
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.
Eight stages, running continuously.
Understand
Discover AI systems, data, applications, processes, technologies, risks and controls, and establish ownership for each.
Connected modules over one model.
Govern every AI system, and everything it touches.
Know what AI is in use, who owns it, and on what basis it was approved to run.
Connect governance with economics.
Know whether the AI investment is worth doing — and under what conditions.
See the enterprise as it is, not as it was reported.
One current view of the enterprise, its AI workforce and everything currently off-track.
Understand where AI lives in the enterprise.
Capabilities, applications, technology and data, with the dependencies between them.
Know what your AI depends on — and whether it can be trusted.
Ownership, meaning, quality, sensitivity and maturity, connected to the AI that consumes them.
Keep risk attached to the system and decision that created it.
Risk, controls, exceptions and important business services in one connected structure.
Model the consequences before committing to change.
Test technical, operational and regulatory change against the connected model.
Make complex decisions visible, explainable and defensible.
Options → Assumptions → Dependencies → Risks → Impacts → Costs → Benefits → Outcomes.