API invoices tell you what AI consumed. They don't tell you what it produced. Kinetome's AI Efficiency Intelligence connects usage and operating costs to measurable business outcomes — so AI spending becomes a decision, not a leap of faith.
A per-workflow view of consumption, reliability, and value — with measured numbers and estimates visibly separated.
Quote-request intake — efficiency
Last 30 days
Total AI spend
$38.74
1.9M input · 0.4M output tokens
measured
Successful executions
1,176
96.1% success · 31 failed · 16 retried
measured
Cost per successful task
$0.033
Failed + retry cost: $1.62 (4.2% of spend)
measured
Time saved vs. manual baseline
≈235 hrs
Baseline: 12 min/task, customer-provided
estimate
Estimated labor value
≈$7,990
At $34/hr loaded labor rate
estimate
API-cost ROI, 30 days
≈205×
Excludes implementation & maintenance — see total-project ROI
estimate
Optimization flagged: 11% of spend comes from one document type with a low extraction rate — a model routing change is projected to cut it.
Every figure above is illustrative sample data. Real dashboards label each number as measured, estimated, or sample — and show the assumptions behind every estimate.
"Is the AI performing this task economically?"
Token consumption, cost per execution, retry waste, and model selection. Measured directly from provider usage data. This is where most quick savings hide — oversized models, bloated prompts, and silent retry loops.
"How much manual effort does the workflow remove?"
Successful executions multiplied against the manual baseline: how long the task used to take, and how often it happens. The baseline comes from your team — we record its source and treat the result as an estimate until it's validated.
"Is the value worth the cost?"
Measured or estimated business value compared against relevant costs. We report API-credit ROI and total-project ROI (including implementation and maintenance) separately, because they answer different questions at different times.
ROI (%) = measured business value − AI operating costAI operating cost × 100
Simple formula, honest inputs. Operating cost is measured from provider usage records. Business value is modeled — typically from time saved at a labor rate you confirm — and is labeled as an estimate until real outcome data supports it. The measurement period, the baseline, and every assumption are shown with the number.
Three things we will not do: report ROI when the underlying data is incomplete, present an estimate as a measurement, or bury implementation and maintenance costs to make API-level ROI look like the whole story.
And one thing we will: tell you when the answer is "this workflow isn't paying for itself yet, and here is why."
Usage and cost records come from the AI providers and automation platforms you use, normalized into a common format: provider, model, workflow, tokens where available, cost, status, retries, and duration. Providers report usage differently — some meter tokens, some credits, some requests — so the pipeline is built to ingest each provider's format rather than pretending they are all alike.
Integrations are configured per customer during an engagement. Your dashboard only ever shows data from sources that are actually connected — if nothing is configured yet, it says so plainly instead of showing invented numbers.
That question is exactly what this service answers. Tell us what you're running and we'll scope what can be measured.