ShelfPilot is an autonomous AI agent that makes buying decisions for your store. It analyzes sales history, spots trends, allocates product, and keeps your shelves optimized, all without a spreadsheet in sight.
Buyers spend hours in spreadsheets guessing what to stock next season based on gut feel and last year's numbers.
By the time a human spots a trend shift, the window has closed. Dead stock piles up. Winners sell out.
Poor allocation costs retailers 4-8% of annual revenue. That's money left on the table every single day.
ShelfPilot analyzes your sales history, seasonal patterns, and market trends to determine exactly what to buy, how much, and when. It doesn't recommend. It decides.
While human buyers review data quarterly, ShelfPilot monitors shifts daily. It catches emerging trends before your competitors and adjusts allocations on the fly.
No more warehouses full of last season's mistakes. ShelfPilot predicts slow movers before they become dead stock and reallocates budgets to what actually sells.
Every product category gets its own buying strategy based on historical performance, margin targets, and velocity data. Granular, not generic.
ShelfPilot was born from 8 years of hands-on retail buying experience in specialty retail. Every algorithm is informed by real buying decisions, real allocation mistakes, and real revenue outcomes. This isn't another inventory dashboard, it's the buyer you wish you could clone.
It needs an employee who works around the clock, learns from every sale, and never makes the same buying mistake twice. ShelfPilot is that employee.