Predict Demand with 95% Accuracy

Our AI forecasting system typically achieves 90-95% accuracy for established products with sufficient historical data. Accuracy varies by industry, product lifecycle stage, and market volatility. We provide detailed accuracy metrics by product category and confidence intervals for each prediction, allowing you to make informed decisions. The system continuously improves as it learns from new data, with many clients seeing accuracy improvements of 10-15% within the first six months of implementation.

At minimum, you need 12-24 months of historical sales data including product SKUs, quantities, dates, and ideally prices. The more data you provide, the better the forecasts. Additional valuable data includes promotional calendars, seasonal events, inventory levels, and external factors like weather or competitor activity. We can work with data from various sources including ERP systems, point-of-sale platforms, e-commerce systems, and spreadsheets. Our team assists with data preparation and quality assessment during implementation.

Yes, our AI uses several techniques for new product forecasting. We can apply patterns from similar existing products, analyze category-level trends, incorporate market research data, and factor in planned marketing activities. For product line extensions, we leverage historical performance of parent products. While new product forecasts have higher uncertainty than established items, our approach provides significantly better predictions than simple estimation methods. The system automatically adjusts forecasts as actual sales data accumulates.

Our algorithms automatically detect and model multiple seasonal patterns including weekly, monthly, quarterly, and annual cycles. The system distinguishes between repeating seasonal effects and underlying growth or decline trends. For holidays and special events, it learns the specific impact patterns and timing. The forecasting engine can handle products with complex seasonality, such as fashion items with both seasonal and fashion cycle patterns. You can also manually input known future events to enhance forecast accuracy.

Most implementations complete within 4-6 weeks. Week 1 involves data integration and quality assessment. Weeks 2-3 focus on model training and initial validation. Week 4 includes user training and pilot testing with a subset of products. Weeks 5-6 involve full-scale deployment and monitoring. The timeline varies based on data complexity, number of SKUs, integration requirements, and your team's availability. We provide project management support throughout the process to ensure timely completion and successful adoption.

Pricing depends on factors including number of SKUs, forecast frequency, data volume, and integration complexity. We offer flexible plans starting at basic packages for small businesses with a few hundred products, up to enterprise solutions for organizations with millions of SKUs across multiple locations. Most clients see ROI within 3-6 months through reduced stockouts, lower inventory carrying costs, and improved cash flow. Contact our sales team for a customized quote based on your specific requirements and volume.