Data audit and problem framing
We check whether your data holds a signal to forecast from. This is the first and most honest stage: if there is no signal, we will say so before you spend budget on a model.
Forecasting · ML · Data analytics
Predictive analytics: solutions that calculate rather than guess
A forecast is useful exactly as far as it can be trusted when a decision is made. We build models whose accuracy is measured, whose limits of applicability are known and whose behaviour when the data fails is understood.
For companies where decisions on purchasing, production or hiring rest on an estimate of future demand, load or risk — and where a forecasting error costs money.
What is included
Demand and sales forecasting
Time-series and gradient-boosting models that account for seasonality, promotions, the calendar and external factors. We compare against a naive baseline — if the model does not beat it, the model is not needed.
Predictive equipment maintenance
Predicting failures from telemetry: event labelling, feature selection, estimating the cost of type I and type II errors — a false alarm and a missed failure cost different amounts.
Risk and churn assessment
Scoring models with explainability: why the model gave this score, which features drove it and when its decision must not be applied.
Deployment and operation
The model in production: data pipeline, regular retraining, drift monitoring. A forecast degrades over time — it has to be watched, not “handed over and forgotten”.
How we work
Data diagnostics
We look at what there is: completeness, history, labelling quality. We estimate the achievable accuracy before work starts.
Baseline
We build the simplest possible solution. It becomes the bar: anything more complex must beat it, otherwise it makes no sense.
Model
We improve, iteration by iteration, a metric tied to your economics rather than to abstract accuracy.
Production
Pipeline, monitoring, retraining. We hand it over with documentation and train your team.
Why us
Engineers and researchers
We go all the way from a research paper to a working service. Our own libraries and models — not a retelling of other people’s solutions.
An honest metric
We measure the model against a naive baseline and say when the task cannot be solved with the data available. We report a negative result rather than hide it.
Full cycle
Data, model, infrastructure, interface — one team, with no handing off of responsibility between contractors.
Cases
Frequently asked questions
How much data do we need for a forecast?
It depends on the task and the seasonality: a demand forecast usually needs a history covering 2–3 full seasonal cycles. We give an exact answer after the data diagnostics — a separate short stage that shows whether the required accuracy is achievable at all.
How is predictive analytics different from reports and BI?
BI answers the question “what happened”; predictive analytics answers “what will happen, and with what probability”. They are different tools: BI describes the past, while a model estimates the future and always carries an error that has to be accounted for in the decision.
What if the model is wrong?
The model will be wrong — the only question is how often and how expensively. So from the start we calculate the cost of an error and design the solution so that expensive errors are rare, even at the price of more frequent cheap ones.
Can the model be deployed inside a closed perimeter?
Yes. We work with on-premise deployment when data cannot leave the company perimeter.
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AI implementation for business: we start with the process, not the model
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Cost the effect on your own numbers
AVA is a process economics calculator. In a couple of minutes it shows whether this service pays off in your case — before you talk to us, with no commitment.
Estimate the impact with AVATell us about your task
Tell us what needs solving. If it cannot be solved or will not pay off, we will say so straight away, before any work starts.
or email us directly: hello@xteam.pro