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Method
AI Cost & Quality Engineering
Compare configurations on your tasks, not on model promises.
The relevant measure is the cost per successful task or per validated business outcome. A drop in token consumption is not enough if errors, corrections or retries increase. Our method tests systems against their real results, within the client's security and quality constraints.
Choose a use case to compare01
Observe
Gather usage, traces, model and tool calls, durations, errors, retries and billed costs. Identify what can really be attributed and optimised. Sensitive data is protected and its use in testing must be authorised.
02
Benchmark
Build representative, reproducible tasks, define acceptance rules and establish a baseline for cost, quality and time. Test in an isolated environment any actions that could have external effects.
03
Compare
Use an automated test bench, adapted to the client's system, to measure variants of models, prompts, skills, harnesses, tools and loops. Also compare the effect of simplifications made possible by new models.
04
Verify
Measure the cost per successful result, the success rate, regressions, processing times and human intervention. Deterministic tests are preferred where they exist. Human validation remains necessary for some criteria.
AI FinOps governance: above and throughout the cycle
Economic governance determines the relevant unit, budgets, responsibilities, trade-offs and the net value expected after implementation. It frames every step. If an optimisation is adopted, its rollout is controlled, then its effects are tracked in real usage.
What we compare, and what we do not promise
- We compare similar tasks with explicit success criteria.
- We distinguish test costs, potential unit gains, genuinely reducible spend and observed invoices.
- We take into account rework, human corrections, latency and security.
- We do not sell off-the-shelf software, nor an automatic reduction of your bill.
- We do not deploy any production change without agreement and scoping.