We connect data, evaluate process changes, and show their impact on costs and profit in rubles.
Find what to improve. Measure the savings.
Yardline connects business process data through ETL, helps identify delays and unnecessary costs, and measures savings after a change.
Connect the data
Bring together requests, activities and costs from operational systems. ETL cleans and links the data by case, business unit and time.
Systems → process data
Find inefficiencies
Trace each request: where it waits, gets sent back or requires extra work. Compare the time and cost of each stage to choose where to improve.
Process stages → opportunities
Evaluate the change
Changed a workflow, a rule or an automated step? Causal inference compares the outcome with a defensible no-change scenario, accounting for workload, seasonality and differences between teams.
Change → attributable impact
Calculate net savings
Express cost reductions in rubles and subtract implementation costs. See the calculation, uncertainty and limitations. Released staff hours are reported separately from cash savings.
Cost reduction − implementation → savings
A demonstration prototype is available today. Data sources, metrics and comparison methods are agreed for each pilot. ML is used for specific tasks, such as predicting delays.
What is behind that final number.
Suppose a company changes how purchase requests are processed. Costs fall. How much came from the new workflow, and how much from a change in request volume?
This example illustrates the financial calculation. It does not demonstrate the outcome of an actual implementation.
6,400,000 − 3,900,000 − 700,000 = RUB 1,800,000 over the period.
In a real evaluation, the no-change scenario must be justified: for example, by comparing cost changes in pilot and comparison units using difference-in-differences.
No confidence interval has been estimated for this example. A rollout recommendation requires checks of the data, assumptions and robustness of the result.
Illustration, not a client result
Lower costs are not proof of impact.
Seasonality, transaction volume and other initiatives also affect the numbers. Causal inference helps separate their influence from that of the process change.
What we compare against
Comparable units without the change, or a defensible synthetic scenario. A simple before-and-after comparison is not enough.
When the result is credible
When groups and periods are comparable, rollout dates are known, pre-change trends have been checked and other material events have been considered. These conditions inform the choice of DiD or synthetic control.
What counts as money
A supported change in costs or profit, less implementation costs. Released hours are shown separately: they do not automatically become cash savings.
When the data are insufficient
We document the limitation and the missing data. If the effect cannot be reliably identified, we do not recommend a wider rollout.
