AI purpose-built for grocery refers to machine learning models that are trained specifically on grocery and fresh data. Nearly a decade ago, we saw that traditional systems couldn't truly handle the messiness and uncertainty inherent to grocery data.
So we engineered a new solution for fresh, because it was the most complex, the most profitable, and the most underserved. Then we expanded to the rest of the store.
Afresh runs on proprietary models trained on billions of department- and item-specific inputs to make smarter, more profitable recommendations for every item type—from the fresh perimeter to center store and from stores to distribution centers.
Adaptive Data Models
Afresh continuously reconciles and corrects data inputs—like different SKUs, shelf lives, and mismatched measurement units—to build a reliable decision-making foundation.
Probabilistic Decision Making
Afresh models probability curves for demand, inventory, and perishability to better simulate and choose scenarios.
Automated Workflows
Afresh automates routine decisions and surfaces the ones that need human judgment.
Data validation through continuous cleaning and reconciliation
Most AI was built for simple barcodes, where one box of cereal stays the same from order to sale. But for most grocers, their source systems rarely agree on what an item is. A single whole chicken can appear as 10 SKUs across vendors, pack sizes, and legacy codes.
Afresh understands and normalizes the relationship between all these items. The system clusters ERP, WMS, and POS metadata into a single entity (the whole chicken). This item is recognized once and tracked through every form it takes in-store: rotisserie chickens, packs of thighs, and shredded chicken in deli soup.
By cleaning and normalizing inputs, Afresh builds a more accurate representation of reality on which to build every forecast and order downstream.
See how Afresh handles data management →
Probability-based forecasting, inventory, and perishability modeling
Typical ordering systems produce a single demand forecast and add a fixed safety stock buffer. That works with steady sales and long shelf lives. But in fresh, demand swings unpredictably, on-hand inventory deviates from what the system shows, and every item has a finite shelf life that turns into shrink if it isn’t sold in time.
Afresh models probability distributions for demand, inventory, and perishability on every item, factoring seasonality, promotions, day-of-week patterns, weather, and historical variability. For each ordering, buying, and production decision, the system simulates thousands of scenarios and weighs stockout risk, shrink, and labor cost against expected profit. The recommendation with the highest expected profit is what the operator sees.
By modeling the full range of outcomes instead of a single estimate, Afresh produces recommendations with a measurable confidence level on which every downstream workflow depends.
Explore demand forecasting →
Explore Intelligent Inventory →
Routine decision automation and exception-based reviews
Afresh executes high-confidence recommendations automatically: from order submissions to pre-populated inventory estimates to production schedules. Lower-confidence cases are flagged for review to get human judgment where it actually matters and train the self-learning AI models.
By matching automation to the system's confidence in each decision, Afresh keeps store teams focused on the exceptions that require their attention, which drives our 94% recommendation adherence.
See how Afresh coordinates decisions across the supply chain →
Discover why Afresh is the cornerstone solution for leading retailers.