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System of Commerce

Control what the institution buys, who supplies it, and what dependency it creates.

Bring supplier assurance, procurement, contracts, compute, capacity and commercial terms into the same operating model as AI governance and economics.

Discuss AI Procurement & Suppliers See the full platform
Why institutions need it

The problem is already operating inside the organisation.

Cybatar is designed for material AI activity: systems, decisions, spend and dependencies that can create institutional consequence if they remain fragmented or ungoverned.

01

Procurement evaluates commercial terms while AI governance evaluates risk in a separate process.

02

Supplier evidence is repeatedly collected by different teams.

03

Compute and capacity commitments can create strategic dependency before the AI portfolio is mature.

04

Contract renewals are disconnected from actual usage, value and assurance outcomes.

What Cybatar provides

Institutional capability, not another isolated dashboard.

01

Supplier Trust Exchange

Maintain reusable supplier assurance profiles, obligations and evidence rooms.

02

AI Procurement Orchestration

Coordinate requirements, suppliers, evaluation and governance around material AI purchases.

03

Compute & Capacity Markets

Evaluate and manage infrastructure, capacity, resilience and commercial exposure.

04

Contract Lifecycle

Connect contractual obligations and service performance to operational AI dependency.

Institutional outcomes

What changes when ai commerce becomes part of the operating model.

01Faster supplier diligence
02Lower duplicated assurance effort
03Clearer dependency risk
04Commercial decisions linked to actual value
Connected by design

The control plane should sit across the AI estate—not require the estate to move into one vendor.

Cybatar is designed to connect institutional records, providers and workflows while preserving a common governance and evidence model.

Procurement Supplier Portal AI Economics Infrastructure and FinOps
The wider platform

Every operating layer works against the same AI estate.

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System of RecordAI Estate

Create a canonical, continuously maintained inventory of models, agents, applications, suppliers and material AI use across the institution.

 System of ControlAI Control

Route material AI activity through intake, authorisation, policy, regulatory obligations, review and exception handling before risk becomes operational dependency.

 System of EvidenceAI Evidence

Preserve interaction, decision, source, calculation, tool, moderation and review evidence so material AI facts can be reconstructed and independently inspected.

 Continuous AssuranceAI Assurance

Combine model governance, review queues, moderation, incidents, controls and ongoing assurance so risk management does not stop at initial approval.

 AI EconomicsAI Economics

Connect provider usage, token spend, infrastructure, suppliers, programmes and validated business outcomes into one economic view of the AI estate.

 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.
Next step

Discuss AI Procurement & Suppliers

Bring the current operating context. Cybatar will structure the estate, decision problem, evidence requirement and next practical step.

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