Leverage historical data, market trends, and external factors to accurately predict future demand – no guesses, just data.
Connect your data in CSV, JSON, or direct export formats – no complex setup or unnecessary obstacles.
We combine traditional machine-learning models with advanced AI Deep learning architectures for maximum accuracy.
Minimize shortages, avoid excess inventory, and streamline operations through data-driven analysis.
Whether you are a small business or a global enterprise, our solution adapts to your needs and grows with you.
Understanding Your Business
Focus on understanding your goals, challenges, and data structure. This step ensures that our predictive solution fully meets your needs.
Preparation for the Test Phase
The meeting results in a clearly defined Proof-of-Concept (POC) brief and verification that your data is ready for AI prediction.
A free Proof-of-Concept is used to validate the potential of AI prediction in your environment.
Data evaluation and preprocessing Model selection and tuning Testing on a real data sample Final report
Defining Success Criteria
We analyze the POC results to ensure they align with your business needs. Key metrics (KPIs) for measuring the accuracy and impact of predictions are finalized.
Transparent Pricing Model
Based on the complexity of the requested solution, we provide a clear and structured pricing proposal.
After approving the POC results, we proceed with full-scale implementation.
Enhancement of features, coding, and hyperparameters based on insights from the POC phase.
Optimization of the best-performing model for maximum prediction accuracy.
Testing against a reference period to ensure the model aligns with real-world trends.
Ensuring integration, performance optimization, and continuous monitoring of results.
Data Flow Automation – connecting with your systems for seamless data delivery. Model Oversight and Maintenance – regular checks of accuracy and adjustments to changes in data. Support and Development – ability to scale the model, add new features, and customize to specific needs.
Historical records (sales, inventory, demand) in a structured format for model training.
Defining a real goal (e.g., inventory optimization, waste reduction, improved demand planning).
A brief introductory meeting where we discuss your requirements, available data, and define key success metrics.
Defining how the success of predictions will be measured (accuracy, cost savings, efficiency improvements).
Sharing relevant data under an NDA, testing AI predictions, and continuously refining the approach based on results.
Inventory prediction powered by artificial intelligence helps you reduce losses and increase your business efficiency. With accurate demand analysis, you’ll always have the right amount of stock on hand. You’ll minimize the risk of shortages and overstock, saving both time and costs. AI continuously learns from your data, adapting to seasonal trends and market changes. Gain a competitive advantage and peace of mind knowing your inventory works for you, not against you.
Save Time and Money
With AI-powered inventory forecasting, you’ll significantly reduce the time spent on planning and warehouse management. Smart inventory control also lowers costs and increases your business profitability.
Gain an Edge Over the Competition
With AI inventory forecasting, you’ll always stay one step ahead and be ready to respond faster than your competitors. Modern technology ensures stable supply and greater customer satisfaction.
Example of Historical Sales and Inventory: excesses, shortages, and savings achieved through AI forecasting.
Black Line – Actual Sales
Green Line – AI Forecast
Red Area – Excess Inventory Red Hatching – Inventory Savings Thanks to AI
Yellow Area – Stock Shortage Yellow Hatching – Stock Replenishment Thanks to AI
The chart shows a comparison of historical sales with the AI model simulation during training. Predictions are calculated monthly based on historical data, item similarities, and the impact of promotional campaigns.
Blue Line – Actual Sales Red Line – AI Forecast
This chart illustrates the final sales prediction, comparing the results of the POC prediction, the final model, and the customer’s existing estimate.
Black Line – Actual Sales Red Line – Customer Prediction Blue Line – POC Prediction Green Line – Final Prediction
Inventory Forecasting with AI 10. Final model Final Test – Full Dataset Moment of truth: How does the model perform when run on all 15,000 items? Model and data configuration (based on previous findings): Feature...
Inventory Forecasting with AI 9. Forecasting Short-History Items Summary In the previous chapter, I trained the model on the entire assortment for the first time – not only SKUs with a full 36-month history, but...
Inventory Forecasting with AI # 8. Generalization – Including Items with Incomplete History Full Dataset Test In the previous chapter, we confirmed that the model performs “flawlessly” for the 70% of the assortment with a...
Inventory Forecasting with AI 7. Feature Engineering II – How to Tame Seasonality, Zeros, and Promotions Seasonal Sales A classic example: Christmas products. They don’t sell at all for ten months, and then sales suddenly...
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POC Free
The price depends on the complexity of the model:
Do you have questions about our AI-based inventory management system or are you looking for a customized solution?
We’ll be happy to provide more information and show you how our technology can streamline your business.
Contact us, and together we’ll find the best way to optimize your inventory.
Jičínská 226/17, Praha, Žižkov, PSČ 130 00, Česká republika
Jednatel: Věra Vítová
sales@neebile.cz
(910) 658-2992
IČO: 172 28 018
DIČ: CZ 172 28 018
Data Box ID: ykwdnxf
sales@neebile.cz
Jičínská 226/17, Praha, Žižkov, PSČ 130 00 Česká republika
(910) 658-2992
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