Connected modules. One enterprise model.
Each module reads and writes to the same model, so an economic assumption, a data issue, an architecture dependency and a governance decision all describe the same enterprise.
Govern every AI system, and everything it touches.
One accountable register of AI in use, each entry with an owner, an assessed position and a recorded decision.
Know what AI is in use, who owns it, and on what basis it was approved to run.
How it worksHide detail
- What it does
- Registers AI models, agents and AI-enabled applications, assesses their risk and impact, and records approval with named accountability.
- Why it matters
- AI is adopted faster than governance can follow when registration, assessment and approval sit in separate places.
AI Governance · capabilities
- Model inventory
- Agent oversight
- Risk & impact
- Approvals
- Lifecycle state
- Ownership
Connect governance with economics.
Investment choices are made with risk, control and economic consequence visible together.
Know whether the AI investment is worth doing — and under what conditions.
How it worksHide detail
- What it does
- Models the cost, benefit and sensitivity of an AI or change decision and attaches the result to the decision record.
- Why it matters
- Governance decisions are investment decisions; separating them leaves boards approving risk without economics.
AGEE · capabilities
- Cost
- Benefit
- ROI
- Payback
- NPV
- IRR
- Sensitivity
- Scenarios
See the enterprise as it is, not as it was reported.
Leadership acts on the current position, with exceptions raised rather than discovered.
One current view of the enterprise, its AI workforce and everything currently off-track.
How it worksHide detail
- What it does
- Maintains a connected, current representation of the enterprise and surfaces exceptions, agent behaviour and operational signals against it.
- Why it matters
- Executive reporting usually describes a position that has already changed by the time it is read.
Digital Twin & Operational Intelligence · capabilities
- Digital twin
- AI workforce
- Agent observability
- Exception management
- Operational signals
Understand where AI lives in the enterprise.
Change is planned against real dependencies, so consequences are understood before commitment.
Capabilities, applications, technology and data, with the dependencies between them.
How it worksHide detail
- What it does
- Holds the capability, application, technology and data architecture, and analyses dependencies in both directions.
- Why it matters
- Risk, resilience and regulatory exposure cannot be judged without knowing what depends on what.
Enterprise Architecture · capabilities
- Strategy
- Capabilities
- Processes
- Applications
- Data
- Technology
- Infrastructure
- Suppliers
Know what your AI depends on — and whether it can be trusted.
AI runs on data whose origin, quality and permitted use are known and owned.
Ownership, meaning, quality, sensitivity and maturity, connected to the AI that consumes them.
How it worksHide detail
- What it does
- Catalogues data assets, agrees business meaning, monitors quality and tracks governance maturity and automation.
- Why it matters
- Most AI risk originates in data. Treated separately, a quality issue never reaches the AI decision it undermines.
Data Governance · capabilities
- Ownership
- Classification
- Quality
- Lineage
- Sensitivity
- Maturity
- Automation
Keep risk attached to the system and decision that created it.
Exposure is prioritised on evidence, and control weakness is visible before it is tested.
Risk, controls, exceptions and important business services in one connected structure.
How it worksHide detail
- What it does
- Scores operational, regulatory and model risk from real dependencies, maintains the control library, routes exceptions and maps service resilience.
- Why it matters
- Risk assessed in isolation from its cause cannot be prioritised or mitigated with confidence.
Risk, Control & Operational Resilience · capabilities
- Risk scoring
- Controls library
- Exceptions
- Important business services
- Third-party dependencies
Model the consequences before committing to change.
Decisions are taken with the downstream consequence examined in advance.
Test technical, operational and regulatory change against the connected model.
How it worksHide detail
- What it does
- Runs scenarios across the enterprise model and reports the connected risk, control, resilience and economic impact.
- Why it matters
- Large change is usually approved on assertion; simulation replaces assertion with a traceable analysis.
Simulation & Scenario Intelligence · capabilities
- New AI system
- Application replacement
- Regulatory change
- New autonomous agent
- Technology change
- Operational disruption
Make complex decisions visible, explainable and defensible.
Any governance position can be evidenced as at a point in time, without manual reconstruction.
Options → Assumptions → Dependencies → Risks → Impacts → Costs → Benefits → Outcomes.
How it worksHide detail
- What it does
- Structures decisions into records, holds the supporting evidence and maintains a traceable audit history.
- Why it matters
- Governance is judged on whether a position can be reconstructed later, not on whether it was described well at the time.
Decision Intelligence, Evidence & Audit · capabilities
- Options
- Assumptions
- Decision records
- Evidence vault
- Audit trail
- Reporting