Make decisions based on
accurate AI predictions
Stop guessing the future. We train machine learning models on your historical sales data to accurately predict product demand, prevent stockouts and identify customers who are about to leave, long before they actually leave.
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Sales and demand forecasting with accuracy up to 92%
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Optimization of inventory levels and prevention of capital freezing
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Early Client Churn Detection (Churn Rate Prediction)
Why is this critical right now?
Frozen funds on the warehouse
You are ordering excess goods that have been lying for months, flushing out working capital from the business.
Unexpected shortage (Out of Stock)
A hot product unexpectedly runs out during peak season, and you lose hundreds of thousands of profit.
Silent leakage of permanent clients
Clients gradually reduce orders and move to competitors, and you notice this when it's already too late.
What does it give to business?
Why is the implementation of this technology taking off in the first months.
Optimal procurement schedule
AI accurately calculates the date and quantity of the next order from the supplier taking logistics into account.
Seasonality and Trends Consideration
Algorithms analyze holidays, day of the week, currency fluctuations, weather, and market trends simultaneously.
Segmentation of LTV and client value
Identification of the most marginal client cohorts for marketing budget optimization.
Prevention of cash breakdowns
Financial forecasting model warns of liquidity deficit 30–60 days ahead
Automatic Recommendations for Actions
The system does not just draw graphs, but forms clear instructions: "Make a discount," "Order 300 units".
Full Integration with ERP/BI
Export of forecasts to PowerBI, Tableau, Google Looker or directly to 1C/BAS/ERP.
How We Implement It
No magic. Just a clear engineering process
Audit of historical data: analysis of quality, completeness and structure of your sales databases for the last 1–3 years.
Cleaning and Feature Engineering: forming key metrics, removing anomalies and spikes
Training and validation of models: testing ensembles of algorithms (XGBoost, Prophet, LightGBM, neural networks).
Backtesting on past periods: checking the accuracy of the model on "blind" business test data.
Creating an automatic pipeline: setting up daily recalculations of forecasts with new data.
Deploying dashboards: visualization of conclusions in a format understandable for managers.
Frequently asked questions
How much historical data is needed for model training?
For a quality forecast, it is desirable to have a sales history of at least 6–12 months. The larger the sample, the more accurate the result.
What is the difference from regular Excel reports?
Excel shows what has already happened in the past. Predictive models calculate nonlinear probabilities of what will happen in the future, taking into account hundreds of interconnections.
Can the model make mistakes during force majeure events?
During sharp macroeconomic shocks the model requires calibration, yet it adapts to new trends dozens of times faster than human intuition.
Where is the analytical system deployed?
In the cloud (AWS / Google Cloud) or on your own servers for the full confidentiality of financial indicators.
How long does the model development take?
From initial data analysis to the first working forecast — usually between 3 and 5 weeks.
Ready for Implementation?
Describe your task. We will conduct a free audit and propose an AI solution architecture specifically for your business.