Illustrative and anonymized representative case. It describes a common enterprise delivery pattern, not a named customer engagement or a promise of outcome.
The situation
The decision to make
Several departments had introduced AI features independently. Usage was increasing, but there was no common way to see which models served which workflows, what each task cost or where quality trade-offs were being made.
The representative engagement focuses on a platform team that supports multiple business units, customer-facing products and internal copilots. The aim is not merely to reduce spend, but to make cost, performance and quality decisions traceable.
CoreShift approach
A staged implementation path
Create a shared attribution model
Label usage by customer, department, product workflow, Agent and model so that every request can be placed in business context.
Pair cost with quality
Bring evaluation signals and operational latency alongside token and provider usage instead of treating cost as an isolated metric.
Turn insight into operating choices
Use model-routing rules and review thresholds to guide decisions about defaults, exceptions and high-value workloads.