Case Studies
The examples below show the work mt.st is built to do: reveal true cost, improve pricing, and structure commitments before spend becomes permanent.
Representative scenario
Usage had moved faster than procurement. Finance could see the total, but not the inefficiency inside model choice, retries, and oversized calls.
Representative scenario
The buyer expected rapid usage growth but lacked confidence in provider terms. Capacity, variance, renewal risk, and alternatives were modelled before commitment.
Representative scenario
High-cost models were being used for routine generation, classification, and research. The review created a tiered routing plan with governance by workload.
Discretion
AI spend reveals product strategy, procurement leverage, and future operating plans. Outcomes are reported at the level decision makers need. The detail remains discreet.
Your position
A focused review shows whether the next move is optimisation, negotiation, or forward purchasing.
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