ICSO — AI Model & Token Cost Diagnostic

Tested replay engine
The ICSO replay engine has been validated on external usage-log data, demonstrating stable and deterministic processing across repeated runs.
PROBLEM

AI teams often know total AI spend.
They do not always know why it happened.
Which requests were unnecessarily expensive?
Where was a premium model used for a simple task?
Where did context size, retries, failed tools, or missed cache opportunities increase cost?
ICSO helps teams inspect the spend behind the usage.
Common cost leaks
Expensive models used for simple tasks
Premium models may be used where a lower-cost route could potentially preserve quality and reduce cost.
Too many tokens and oversized context
Large prompts, long context, and unnecessary history can quietly increase cost.
Retry and regeneration loops
Repeated attempts can multiply spend without improving the final result.
Failed or repeated tool calls
Agentic workflows can waste cost through failed tools, repeated calls, and unnecessary execution steps.
What ICSO analyzes
ICSO works from historical AI usage logs.
For a first diagnostic, we do not require production access, live traffic access, or changes to your AI system.
Typical log fields
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Model used
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Input and output tokens
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Estimated request cost
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Context size
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Retry and regeneration counts
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Tool calls and failed tools
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Cache hits and misses
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Latency and errors
ICSO reviews these signals to identify where AI cost may be avoidable, inefficient, or worth testing under a controlled optimization pilot.

What you receive
A replay diagnostic gives your team a clear view of where AI spend may be leaking.
Deliverables
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Usage log validation
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Baseline cost comparison
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Model usage and token-pressure analysis
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​Top candidates for avoidable spend
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Conservative savings scenarios built on transparent assumptions
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Recommendations for a limited production test
ICSO estimates potential savings from historical logs. Actual production savings require a later controlled pilot or before/after measurement.
