Demand forecasting with 90%+ accuracy instead of manual planning
Are your employees calculating "by average"? Our ML model takes into account 30+ factors (exchange rates, promotions,
weather) and shows the real picture. Pilot on your data in 8 weeks.
weather) and shows the real picture. Pilot on your data in 8 weeks.
152
-FZ
Compliance with requirements. Closed perimeter.
8
weeks
Results in 8 weeks
< 1
year
Works with limited history
We will help
Problems we solve
AI forecasting turns uncertainty into a competitive advantage
Problem
Custom development
Money frozen in inventory
Accuracy beyond human capability (90%+)
Wasted budgets on ineffective promotions
Considers 3000+ factors: from exchange rates to competitor promotions
Plan-fact "doesn't match" due to currency fluctuations and weather
Forecasts for 100,000+ SKUs/locations simultaneously
No forecast with limited history → Launching a new SKU is "a shot in the dark"
Ready-to-use figures in your 1C or BI report
Calculator
How much will you earn from better demand forecasting?
Minimum input — maximum clarity. We automatically improve forecast accuracy by +15% compared to
your current one.
Industry
With our solution: 57.5% (that's +15% relative, +7.5 pp)
Additional profit/savings per year
0 ₽
Storage + fewer stockouts + fewer write-offs
Working capital released
0 ₽
One-time, from inventory reduction
First-year potential
0 ₽
Annual gain + released capital
Who is the solution for
Retail and manufacturing of any scale
Retail
- Thousands of SKUs × dozens/hundreds of stores
- OOS (out-of-stock) management
- Seasonality and holidays handling
- Promotion optimization
Manufacturing
- Plan-vs-actual capacity utilization
- Raw material demand forecasting
- Finished goods optimization
- Constraints and shift scheduling
Case studies
Proven results
Demand forecasting
6-12 months
5-7M ₽
6-month weekly forecasting horizon
+3.7%
Revenue
+22%
Orders shipped in full and on time
up to 92%
Forecast accuracy
MAPE 11.2%, WAPE 15.4%
Metrics depend on category and horizon; confirmed during the
pilot
Demand forecasting
3 months
1-2M ₽
3-month weekly forecasting horizon
–4%
OOS for top SKUs in DIY and construction
+12%
to inventory turnover for key categories
up to 91.3%
Forecast accuracy
MAPE 16.3%, WAPE 21.1%
Metrics depend on category and horizon; confirmed during the
pilot
Demand forecasting
4-6 months
2-3M ₽
3-month weekly forecasting horizon
-8%
Excess inventory reduction
-35%
Write-offs reduction
up to 92%
Forecast accuracy
MAPE 14.8%, WAPE 19.6%
Metrics depend on category and horizon; confirmed during the
pilot
How it works
From raw data to accurate
forecasts in 8 weeks
forecasts in 8 weeks
External factors
Weather
Holidays
Exchange rates
Promotions
Logistics
Data
Collecting historical data on sales, inventory and production
Features
Feature engineering and adding external factors
Models
N-BEATS, CatBoost, ensemble methods
Reports
Integration with ERP/BI systems
Deployment
On-premise deployment in your network
Validation
Quality validation via MAPE/WAPE metrics
Cold start
We work with zero or short history —
details discussed during the meeting
details discussed during the meeting
01
Hierarchical models by category and SKU clustering
02
Cross-sectional analogs (similar stores/regions)
03
External factors as a dynamics "anchor"
04
Transfer learning and prior knowledge
05
Gradual self-learning as data accumulates
Forecasting outcomes
Companies that have adopted AI
Companies without AI adoption
according to McKinsey & Company
Integrations and security
Seamless integration with your systems while fully complying with Russian regulations
On-premise / Private cloud
Deployment within your perimeter in Russia
Data localization
All data stays within your infrastructure
Access control
SSO/Active Directory integration
Audit logs
Full visibility into user actions
Full compliance with Federal Law 152-FZ "On Personal Data" and your company's internal
information security policies.
We help with
Pilot in 2 months
Quick start with measurable results
Week 1–2
- Data audit
- Building the data mart
- Defining pilot KPIs
Week 3–4
- Feature engineering
- Model training
- Initial calibration
Week 5–6
- Backtesting on historical data
- Benchmarking against current practice
- MAPE/WAPE/OOS analysis
Week 7–8
- Reports/API integration
- Executive presentation
- Rollout roadmap
Pilot output
- Accurate forecasts and reports
- Economic impact assessment
- Ready-to-execute rollout plan
FAQ
Frequently asked questions
What will I get in the end?
A product tailored to the company’s needs. It includes software, trained machine learning models, and documentation.
What methods do you use?
Machine learning, natural language processing, computer vision, recommendation algorithms, and others.
What is the cost?
The cost is determined by the level of complexity, required technologies, and scope of integration. Leave a request on the website, and we will get in touch, discuss the details, and send you a commercial proposal.
Are specialized skills required to work with you?
No. Our team will explain any unfamiliar terms in plain language and support you at every stage of the collaboration.
How does the product creation process work?
We start with an analysis of your business processes and goals. Then we move on to AI development, create a prototype, test the models, and integrate them into your systems.
How much time does implementation take?
The development duration depends on the complexity and can range from 3 weeks to several months. We will provide a detailed project timeline.
Can I test the product?
Yes, you can test prototypes and pilot programs.
How is effectiveness measured?
We define KPIs, including performance metrics, cost indicators, and service quality.
Will you train our employees?
Yes, we conduct workshops.
What should be done if issues arise?
If any issues arise, contact our support team, which will help with any question.
Get a free audit in 3–5 business days
We'll assess how ready your data and processes are for AI-powered forecasting and provide
concrete recommendations.
