Connect people, automation and AI costs to results your team can actually accept. Then choose and implement a better way to complete the workflow.
Usage, subscriptions and generated outputs do not explain whether delivery improved after review, retries and correction.
A lower price per call can be outweighed by failures, human intervention and a poor acceptance rate.
Suite maintenance, duplicated tools and repeated investigations can absorb the capacity that automation was meant to release.
Scope and acceptance criteria are agreed before work begins.
A defined unit of accepted work, current quality and speed, and costs labelled as measured, allocated or estimated.
Human work, deterministic automation, existing tools and suitable AI models tested on comparable tasks.
A scoped change in the existing environment, including the integrations and review steps it needs.
A before-and-after comparison covering cost, acceptance, rework and latency, with assumptions made explicit.
Imagine a batch with €80 of model and infrastructure cost, €240 of human review and €180 of corrections. If 80 results pass the agreed criteria, the full cost is €500 ÷ 80 = €6.25 per accepted outcome.
Failed attempts stay in the cost total. Implementation, licences and maintenance must also be included when applicable, using a transparent allocation method.
Total workflow cost ÷ accepted outcomes
Read the measurement guideChoose one recurring workflow with enough volume to measure. We agree its acceptance criteria, baseline and evaluation method before making changes.
A result that passes agreed criteria. For regression triage it could be one unique incident with a confirmed classification and useful next action. It is not simply a model response or a completed script.
Released capacity, avoided future costs and actual cash savings are reported separately. Time saved becomes economic value through a specific use, such as additional output or reduced external expenditure.
API usage is priced using applicable token categories and rates. Subscription allocations and local infrastructure costs are identified separately. Failures, retries and human review remain in the total.
The engagement is designed to establish evidence. Improvement depends on the baseline, workload and constraints. We do not promise an unmeasured percentage.
A pilot gives a unit cost, not a payback month. Model setup, the early dip and break-even volume for one QA workflow, and agree a stop rule before go-live.
Read articleToken spend is only one part of AI economics. A practical way to include review, correction and failed attempts in your decision.
Read articleBring the process you want to improve. We’ll help identify a practical starting point.
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