SupaBit
AI-Powered Retail & Supermarket Intelligence
SupaBit is a B2B AI-powered retail intelligence and CRM platform designed for supermarkets and retail businesses. The system connects with business data and uses historical sales patterns, external conditions, and AI-based analysis to predict product demand and drive smarter merchandising decisions.
Intelligence Dashboard
Real-time retail analytics powered by AI
Sales Trend
Demand Prediction
| Product | Revenue | Trend |
|---|---|---|
| Fresh Produce | $12.4K | +8% |
| Dairy | $9.8K | +3% |
| Beverages | $8.2K | +12% |
| Snacks | $6.1K | -2% |
| Bakery | $5.7K | +6% |
Predictive Demand Alerts
AI Demand Alert
Rain is forecast for tomorrow. Based on historical sales data, demand is predicted to increase for rain-related products. Consider moving these products to high-visibility shelves.
| Product | Current Stock | Predicted Demand | Sales Trend | AI Recommendation |
|---|---|---|---|---|
| Umbrellas | 45 units | High ↑ | +34% | Move to front shelf |
| Raincoats | 28 units | High ↑ | +41% | Increase shelf space |
| Hot Beverages | 120 units | Moderate ↑ | +18% | Featured display |
| Waterproof Bags | 15 units | High ↑ | +52% | Priority restock |
| Indoor Games | 65 units | Moderate ↑ | +12% | End-cap placement |
Intelligence at Every Level
Retail Data & CRM Integration
Connect retail and supermarket data into one centralized intelligence layer.
- ▹ Centralize customer & sales data
- ▹ Analyze product performance
- ▹ Track inventory & sales trends
- ▹ Historical pattern analysis
AI Sales Prediction
Predict which products will see increased demand based on historical data and external conditions.
- ▹ Weather-driven demand prediction
- ▹ Historical sales pattern analysis
- ▹ Seasonal trend forecasting
- ▹ Category-level predictions
AI Shelf & Merchandising
Receive AI recommendations for product placement and shelf optimization.
- ▹ Front-shelf product recommendations
- ▹ Underperforming product alerts
- ▹ Visibility optimization
- ▹ Merchandising action tracking
Performance Tracking
Compare predicted vs actual performance and continuously improve accuracy.
- ▹ Predicted vs actual comparison
- ▹ Sales impact measurement
- ▹ Recommendation effectiveness
- ▹ Accuracy improvement tracking
End-to-End AI Retail Intelligence
Data Input
Retail Data + Historical Sales + External Data
Processing
AI Prediction Engine
Output
Demand Forecast
Action
Product Recommendations
Review
Admin Dashboard
Validated During R&D Testing
During R&D testing, selected AI-driven merchandising recommendations showed up to a 20% observed sales improvement in controlled test scenarios. Results may vary based on product category, location, and market conditions.
Interested in SupaBit?
We're looking for retail businesses to pilot SupaBit. Get in touch to explore our R&D program.