What AI exists, where it is used and who owns it?
Enterprise AI Management Platform
One operating layer for the entire institutional AI estate.
Cybatar connects inventory, authorisation, policy, evidence, assurance, economics, suppliers and executive decisions so institutions can run consequential AI as one governed estate.
Vendor-agnostic
Institution-wide
Evidence-led
Built for regulated environments
Institutional AI Estate
One operating truth
ModelsAgentsApplicationsSuppliersCompute
System of RecordAI EstateExplore →
System of ControlAI ControlExplore →
System of EvidenceAI EvidenceExplore →
Continuous AssuranceAI AssuranceExplore →
AI EconomicsAI EconomicsExplore →
System of CommerceAI CommerceExplore →
Executive Control Tower Portfolio · Risk · Spend · Value · Evidence · Decisions
The operating problem
Enterprise AI is now a management problem, not only a technology problem.
Models, agents, SaaS features, suppliers, cloud capacity and business use cases are spreading across institutions faster than traditional governance, procurement and finance systems were designed to manage them.
Who may approve material AI activity and under which conditions?
Can the institution prove what happened and why?
What does AI cost and what value is actually validated?
Which suppliers, models and infrastructure does the institution rely on?
Platform architecture
Six operating layers. One institutional record.
Each layer solves a different executive problem, but all of them operate against the same AI estate so governance, evidence and economics remain connected.
System of Record
AI Estate
Create a canonical, continuously maintained inventory of models, agents, applications, suppliers and material AI use across the institution.
Explore capability → System of ControlAI Control
Route material AI activity through intake, authorisation, policy, regulatory obligations, review and exception handling before risk becomes operational dependency.
Explore capability → System of EvidenceAI Evidence
Preserve interaction, decision, source, calculation, tool, moderation and review evidence so material AI facts can be reconstructed and independently inspected.
Explore capability → Continuous AssuranceAI Assurance
Combine model governance, review queues, moderation, incidents, controls and ongoing assurance so risk management does not stop at initial approval.
Explore capability → AI EconomicsAI Economics
Connect provider usage, token spend, infrastructure, suppliers, programmes and validated business outcomes into one economic view of the AI estate.
Explore capability → System of CommerceAI Commerce
Bring supplier assurance, procurement, contracts, compute, capacity and commercial terms into the same operating model as AI governance and economics.
Explore capability → Executive Control TowerExecutive Control Tower
Bring portfolio, risk, investment, evidence, economics and decision queues together so executive teams can govern AI as an institutional estate rather than a collection of projects.
Explore capability →
Executive Control Tower
Leadership should see decisions, not another dashboard.
Cybatar brings portfolio status, material risk, evidence gaps, AI spend, validated value and executive decision items into one operating view.
Explore Executive Control TowerAI systemsEstateMaterial riskControlAI spendEconomicsEvidence coverageProof
DECISION QUEUEApprovals · Exceptions · Evidence gaps · Investment decisions
Institutional lifecycle
Stay involved from first signal to retirement.
Governance is not a one-time assessment. Cybatar follows material AI through discovery, approval, operation, evidence, optimisation and retirement.
- 01Discover
- 02Register
- 03Assess
- 04Authorise
- 05Deploy
- 06Observe
- 07Evidence
- 08Optimise
- 09Retire
Built for the buying committee
Technology, risk, finance, audit and procurement should not govern different versions of the AI estate.
Chief AI Officer
Run AI as an institutional portfolio—not a collection of experiments.
Give the CAIO one operating model for AI estate visibility, material decisions, governance, evidence, economics and measurable institutional value.
Explore → CIO & CTOBring fragmented AI providers, workloads and infrastructure under one operating model.
Create visibility and control across models, clouds, providers, workloads, capacity, resilience and enterprise AI architecture without forcing the institution onto a single vendor stack.
Explore → Risk, Compliance & LegalTurn AI obligations into controls—and controls into evidence.
Connect applicability decisions, policy, controls, evidence requirements, exceptions, incidents and human review into one governed AI control environment.
Explore → CFO & FinanceKnow what AI costs—and require proof of what it returns.
Connect provider, token, infrastructure, supplier and programme cost to measurable outcomes, finance validation and comparable unit economics.
Explore → Internal Audit & AssuranceMove from requesting AI evidence to having it continuously preserved.
Inspect material AI decisions, runtime evidence, provenance, controls, reviews and chain of custody without relying on post-hoc screenshots and recollection.
Explore → Procurement & Vendor ManagementGovern AI dependency before the institution signs for it.
Bring supplier assurance, commercial terms, contracts, usage, resilience and value evidence into AI procurement and renewal decisions.
Explore →Start with the estate
Find the AI the institution already depends on.
An AI Estate Assessment establishes inventory, ownership, governance exposure, evidence readiness and economic visibility before a larger platform programme begins.