Your AI Spend Findings

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cortave Token Spend Optimisation Assessment
Your Token Optimisation Report

Prepared for your organisation

Prepared on -

Here is your token spend summary, including projected cost savings and estimated environmental impact from reduced token usage.

Executive Summary
Based on the values entered into the calculator, there appears to be a meaningful opportunity to reduce token spend and improve workload efficiency.
Estimated Annual Savings
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Indicative yearly savings based on projected optimisation.
Opportunity Tier
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Indicative classification based on projected monthly savings.
Savings Rate
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Estimated proportion of current spend that may be reducible.
♻️ CO₂ Savings
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Estimated annual CO₂e avoided from reducing unnecessary AI token usage.
Current Spend
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Optimised Spend
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Monthly Savings
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Financial Exposure
Operational Efficiency
Governance Signal
Recommended Next Step
Review prompts, model selection, and high-volume workflows to identify avoidable token consumption and prioritise the fastest savings opportunities.
This is an indicative assessment based on the calculator inputs provided and is intended to highlight potential efficiency opportunities rather than replace a detailed workload audit.
Why this matters
cortave is the AI efficiency layer for enterprise
The enterprise challenge
AI inference spend is scaling faster than the governance frameworks built around it. Without visibility, attribution, or guardrails, enterprise AI deployments can face margin compression and budget volatility because the models are being used, but the infrastructure around them is not keeping pace.
The cortave layer
cortave sits between your AI applications and the LLMs. Policy-controlled routing matches every inference call to the model best suited to the task, improving response accuracy and removing the waste that builds when high-cost models are used for low-complexity work.
Strategic interpretation
The opportunity shown in this report is not simply a pricing issue. It reflects a structural gap between AI adoption and AI governance. As usage grows, unnecessary token consumption becomes embedded in daily workflows, and that inefficiency compounds over time across cost, infrastructure demand, and budget predictability.

cortave introduces the missing control layer. Efficiency guardrails enforce the rules. Attribution connects every token to an outcome. Finance and Engineering gain a shared view of where AI spend is going and what it produces. That changes AI from an opaque cost centre into something measurable, governable, and optimisable.

Token spend can fall by 50–80%, and in workflow-heavy environments, by over 90%. Because each inference call is routed to the most appropriate model rather than simply the most capable one, the infrastructure consumes only the compute each task actually requires. The impact extends beyond cost alone.
Indicative ROI Timeline
0–5 Days
Identify Waste
Establish baseline visibility into current token usage, workload mix, and cost drivers.
5–14 Days
Apply Control
Optimise prompt structures, apply routing rules, and align workloads to fit-for-purpose models.
15–20 Days
Lock In Efficiency
Scale governance, enforce efficiency guardrails, and create a durable AI cost control layer.
Projected Spend Impact
The chart below compares projected current spend, optimised spend, and the savings opportunity indicated by the calculator inputs.
Current
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Optimised
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Savings
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Recommended action
The fastest next step is a focused review of routing logic, model selection, and high-volume AI workflows to identify where avoidable token spend can be removed without affecting output quality.
cortave is infrastructure, not a feature. The same strategic position FinOps occupies for cloud cost governance applied to AI inference.