Guac launched on Y Combinator's "Launch YC" recently!
"More on-shelf availability. Less food waste."
Guac is helping grocers solve food waste with its AI-driven, industry-leading, hyper-accurate demand forecasting.
Founded by Euro Wang & Jack Solomon. The team founded Guac with a belief: grocery food waste is not inevitable. By leveraging AI, they help grocery retailers order the right amount to keep shelves full without creating tons of food waste.
HOW IT WORKS
Guac uses machine learning to accurately predict how much grocery retailers will sell each day. This helps them order the perfect amount of inventory: enough to meet customer demand without sending tons of leftover food to landfill. It tells grocery stores exactly how many avocados they’ll sell next Tuesday.
🗑️ The problem
🔴 Predicting future demand is hard: product-level demand drastically fluctuates each day based on hundreds of factors like weather, road closures, and sports games. Yet, even the largest supermarket chains still use basic regression models or Excel formulas, which means …
🔴 Tons of food waste and empty shelves: which costs grocery retailers in Europe/US $188B and costs our planet 15 million metric tons of greenhouse gas emissions each year.
🥑 Guac's solution
⭐ Industry-leading algorithm that predicts order quantities for each item, each day, and each store location. Guac's average forecast error (MAE) is 0.95 units for current customers, translating to a 38% reduction in food waste.
How?
🟢 More than just ML: Grocery demand is driven by real-world events that ML models don’t know exist. So Guac injects 230+ external variables to actually capture people’s buying behavior …
For example, the Guac team found that sports betting odds help them predict beer sales. When a sports match is predicted to be a close game, it turns out more people watch the game, driving up beer sales!
🟢 Customized for each store location: grocery stores located just 5 miles apart have different customer behavior.
🟢 Inventory ordering optimization: Guac then turns its hyper-accurate predictions into useful inventory order quantities, based on individual SKU-specific requirements: minimum order quantities, promotions, unknown loss estimates, shelf life, supplier lead times, and more.
🟢 Modular integration: Guac integrates its ordering recommendations right into the retailer’s existing inventory ordering software systems and workflow. Whether inventory ordering is done at the HQ-level vs. store-level, Guac fits into the retailer’s system, instead of forcing them to uproot their system to fit into Guac's.
