Industry-leading demand forecasting

Applying cutting-edge AI research to fresh forecasting to accurately predict future sales for each product, at each store, on each day.

Our AI technology

An intelligent brain for item-level forecasting

Developed out of AI research, our proprietary algorithm takes a novel approach to deep learning that goes beyond traditional machine learning: it mimics the way that a human brain learns complex causal patterns at a very granular level. Traditional ML models get easily overwhelmed by too much data, struggling to capture individual nuances and often confusing correlation for causation. Our model architecture allows us to process billions of data points and better identify the causal interactions between the data points.

Forecast accuracy

Real-world understanding of your stores

We ground every forecast in the realities of each store: historical sales, on-hand and delivery patterns, promotion mechanics, local weather, holidays, school calendars, and hundreds of other signals. By continuously learning how these real-world data points drive demand at the item/store/day level, Guac separates true drivers from noise and stays accurate as conditions change.

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Guac Forecast Accuracy

Actual Sales

Guac Forecast

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Examples of forecasting intelligence

Promotions Data

Different promotion types

We incorporate how different promotion types affect sales differently: discounts, multi-unit ("2 for $10"), EDLP, and even social media promotions.

Halo & cannibalization effects

We capture halo & cannibalization effects of promotions to reflect true cross-product impacts.

After-promotion effect

We capture how sales go "back to normal" after a promotion ends.

Weather Data

Local weather data per store

We incorporate weather data for the square mile radius around each of your stores.

10+ weather variables

We look at 10+ variables to contextualize weather, including the difference between weather forecasts and actual weather.

Extreme weather events

We incorporate how snowfall in Texas is different from snowfall in Maine — and we look at proxy datasets (e.g. road closures) to capture extreme weather events.

Local Data

School term dates per store

We incorporate different local school term dates and key events (e.g. prom, graduation) for the specific school district that each of your stores is in.

Demographic data

We use demographic data from each of your stores' zip codes to capture demand drivers like SNAP EBT deposit schedules and cultural holidays.

200+ local data

We look at over 200 local variables to better understand how each of your customers shop.

Tailored to you

We train a custom algorithm that is unique to your stores

We work closely with you to add in any external variables to reflect the realities of your stores, not of an average grocery store.

240+

external data variables

4 weeks

average time spent customizing for each retailer

Meet with our team

We know that every retailer is different and that each department has its own unique needs. Share with us your specific business needs, and we'll build a solution that is perfect for you.

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