What AI are we actually using?
Models, agents, embedded SaaS AI, vendors and shadow AI become one governed estate.
Enterprise AI Management Platform
Run your entire AI estate from one control plane.
Discover every AI system. Govern every material decision. Preserve evidence of what AI actually did. Connect AI spend to measurable institutional value.
Vendor-agnosticEnterprise-wideEvidence-ledBuilt for regulated institutions
Institutional AI estateIllustrative control view
EstateKnownModels · agents · applications
ControlGovernedIntake · policy · authorisation
EvidenceTraceableDecisions · actions · provenance
AI EstateMaterial systems requiring institutional control
Live operating layerCustomer Service AgentAgentic AI · Customer operations
ControlledRisk Decision ModelModel · Financial risk
Evidence readyThird-party AI ServiceSupplier AI · Enterprise workflow
ReviewThe executive problem
Enterprise AI has outgrown spreadsheets, committees and disconnected tools.
As AI spreads across business units, clouds and suppliers, institutions need operating control—not another inventory document.
Who authorised it?
Material AI decisions carry accountable owners, policy conditions, approvals and exceptions.
What is it doing now?
Runtime activity, controls, incidents and assurance are connected to the system that generated them.
Can we prove what happened?
Evidence, provenance, chain of custody and audit history are preserved for consequential activity.
Is it creating enough value?
AI cost, token economics, outcomes and validated value are managed together rather than in separate reports.
Cybatar Research
AI capability is moving faster than institutional reality.
The Cybatar AI Reality Curve tracks where emerging AI capabilities have reached repeatable, governable and economically defensible institutional use—and where the Reality Gap remains wide.
Reality GapTechnical Capability − Proven Institutional Value
SignalCapabilityReality GapLearningEconomic ProofScaleUtility
Technical capabilityProven institutional valueReality Gap
One AI estate. Four institutional systems.
The operating architecture for consequential AI.
Cybatar connects what an institution owns, permits, observes and pays for so AI can be managed as an institutional estate rather than a collection of experiments.
System of Record
Canonical AI inventory, ownership, lifecycle, discovery and AI Passports.
Explore AI Estate →System of Control
Intake, authorisation, policy, regulatory obligations, exceptions and assurance.
Explore AI Control →System of Evidence
Provenance, runtime evidence, decision history, custody and audit-ready records.
Explore AI Evidence →System of Commerce
AI spend, suppliers, procurement, compute, contracts, cost allocation and economics.
Explore AI Economics →Institutional lifecycle
Cybatar stays involved from first signal to retirement.
The control plane follows material AI activity across the complete lifecycle so governance, evidence and economics do not disappear after deployment.
- 01Discover
- 02Register
- 03Assess
- 04Authorise
- 05Deploy
- 06Observe
- 07Evidence
- 08Optimise
- 09Retire
Governance claims are not enough. Institutions need proof.
Cybatar connects decisions, sources, tool activity, controls, human review and integrity records into evidence that can be inspected later—not reconstructed after the fact.
- Evidence packets and provenance
- Chain of custody and immutable records
- Audit Room and regulatory evidence
- Model and supplier assurance
Know what AI costs—and what the institution gets back.
Connect model usage, token spend, infrastructure and supplier cost to outcomes, validated value and executive investment decisions.
- AI cost allocation
- Token economics
- Value contracts and outcome registry
- Reality Curve and investment discipline
Built for the institutional buying committee
Different executives ask different questions. Cybatar gives them one operating truth.
Where is AI going—and is it creating value?
Portfolio & programmes →How do we bring fragmented AI under one operating model?
Architecture & infrastructure →Can we turn AI policy into enforceable institutional control?
Governance & regulation →What are we spending, and what value is actually validated?
Economics & value →Can we prove who decided what, when, and on what evidence?
Evidence & audit →Which suppliers and AI services should the institution depend on?
Procurement & suppliers →Industries
Explore all industries →Designed for institutions where AI decisions carry consequence.
Financial Services
Govern models, agents, customer-facing AI, suppliers and investment economics at the standard expected of regulated financial institutions.
Explore →Insurance
Control AI across underwriting, claims, fraud, servicing, distribution and internal operations while preserving accountable evidence and measurable value.
Explore →Government & Public Institutions
Build accountable AI estates across public services, agencies, procurement, sovereign infrastructure and citizen-facing systems.
Explore →Healthcare
Govern clinical, administrative and operational AI with strong ownership, evidence, privacy, human oversight and assurance controls.
Explore →Enterprise AI Management
Take control of your AI estate.
Start with an executive briefing on your AI estate, governance exposure, evidence readiness and economic visibility.