PREDICTIVE
ANALYTICS
We forecast demand, sales and workloads using your data. We show uncertainty ranges, test models on past periods and integrate forecasts into planning.
A FORECAST
YOU CAN VERIFY
We start with the decision you need to make: how much to order, where shortages may occur or what workload your team can handle. We check the historical data and compare the model with a simple baseline forecast. Complexity is justified only if it helps your task.
FROM HISTORY
TO AN ACTION PLAN
Demand forecasting
Sales by product, category or location, accounting for available history and seasonality.
Resource planning
Expected team workload, request volume or production load over the agreed horizon.
Scenarios and ranges
A baseline scenario, forecast range and the effect of available external factors.
Quality monitoring
Actual versus forecast, errors by segment and signals that the model needs review.
FROM DATA
TO A WORKING PROCESS
Historical data audit
Check periods, gaps, outliers and changes to recording rules.
Data preparation
Reproducible cleaning rules, features and an agreed set of sources.
Model and validation
A baseline, training and testing without leaking future data.
Operational forecast
Dashboard or API, calculation schedule, version history and update rules.
PLAN
WITH UNCERTAINTY IN MIND
Forecasting workspace concept: actual and forecast demand, ranges, scenario comparison and past-period error. All screen figures are illustrative.
FROM HYPOTHESIS
TO A WORKING AI SOLUTION
At every stage, we agree on the outcome and the next step.
Brief and decision
We define the management task, forecast horizon and level of detail.
Outcome: defined problem
Data audit
We check the history, sources and potential limitations.
Outcome: readiness assessment
Baseline forecast
We build a simple baseline and define the comparison method.
Outcome: measurable baselineModel and interface
We test models on past periods and design the interface.
Outcome: pilot quality assessment
Integration
We connect calculations to data and the team’s workflow.
Outcome: regular updates
Monitoring and improvement
We monitor deviations from actual results and the rules for model review.
Outcome: a managed forecastCHOOSE THE RIGHT FORMAT
FOR YOUR GOALS
AI projects start at $11,780. The final estimate depends on the task, data and integrations. Recurring model and infrastructure costs are calculated separately.
AI audit
To choose a task and assess feasibility.
Custom quotefor the agreed scope- Process and data review
- Implementation priorities
- Pilot criteria and plan
Pilot solution
To test one practical hypothesis.
from $11,780for the agreed scope- Prototype based on your workflows
- Test set and evaluation
- Operating cost estimate
Implementation and development
To bring the solution into the team's workflow.
Custom quotefor the agreed scope- Integrations and roles
- Monitoring and quality control
- Training and ongoing development
ANSWERS TO
YOUR QUESTIONS
How much does a project cost?
Predictive analytics pilots start at $11,780. After the data audit, we agree on horizons, segments, interfaces and integrations.
What data is needed?
A dated history of the target metric at the level of detail the decision requires. We select additional factors for the task and assess whether the history is sufficient during the audit.
What accuracy do you guarantee?
We do not guarantee a fixed percentage before reviewing the data. We agree on a metric, compare against a baseline method and show errors on a held-out period.
What if there is little high-quality data?
We first determine what can be measured reliably. Improving data collection and reporting can be more useful than immediately building a complex model.
Can we see a forecast range?
Yes, if the chosen method can estimate it correctly. We explain the range and its limits to avoid false confidence.
How do you account for seasonality and promotions?
We include available features and test their usefulness on historical data. For events that have never occurred, we discuss scenario assumptions separately.
Where is the forecast delivered?
In an agreed BI workspace, your system through an API or a regular export. We choose the format for your planning process.
Does the model need retraining?
Quality can change as the business changes. We set up monitoring and criteria for reviewing or updating the model.
A PROJECT HAS
A FACE AND A CHARACTER.
“I personally contribute to strategy and key decisions. A website should do more than impress — it should clearly and honestly explain why a customer should choose you.”
LET’S DISCUSS
YOUR AI PROJECT?
Tell us about the process and available data. We will define the pilot scope, quality criteria and budget.