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AI Economics

Know what AI costs, what it returns, and where the Reality Gap is widening.

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

Request an AI Economics Briefing 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

AI spend grows across providers and business units without a common cost model.

02

Token and infrastructure costs are visible, but business outcomes are not connected to them.

03

ROI claims use inconsistent periods, currencies or attribution methods.

04

Executives cannot distinguish expected value from measured, validated and realised value.

What Cybatar provides

Institutional capability, not another isolated dashboard.

01

Universal Cost Allocation

Allocate AI cost to systems, programmes, business units, suppliers and outcomes.

02

Token Economics

Track input, output, cache and provider units and connect usage to economic outcomes.

03

Value Contracts

Define baselines, targets, measurement methods, owners and finance validation before value claims are made.

04

AI Reality Curve

Compare technical capability with proven institutional value and identify where evidence is still weak.

Institutional outcomes

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

01Transparent AI spend
02Comparable unit economics
03Validated value
04Better investment decisions
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.

Usage and billing Provider pricing Business outcome registry Executive decision items
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.

 System of CommerceAI Commerce

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

 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

Request an AI Economics Briefing

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

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