How AeroGear Unlocked $65,000 in Frozen Capital
By transitioning from "gut-feeling" ordering to predictive Python pipelines, AeroGear eliminated stockouts and slashed warehouse overhead.
The Business Problem
AeroGear, a high-growth outdoor equipment retailer, was facing a classic scaling paradox. During peak seasons, they were losing 15% of potential revenue because their best-selling hiking boots were constantly out of stock. Simultaneously, they were paying $4,000 per month in storage fees for inventory that hadn't moved in six months.
The QueryLess Solution
We built an automated data pipeline that connected their Shopify store and Warehouse Management System (WMS) directly to a custom forecasting engine. Instead of a generic dashboard, we delivered a Precision Demand Forecasting model that analyzed three years of historical sales, regional weather patterns, and marketing spend.
The system automatically generated weekly "Optimal Order Quantities" (EOQ), ensuring they only spent cash on products guaranteed to move.
The Result
Within 90 days, AeroGear reduced their overstock levels by 30%. The accuracy of their inventory allowed them to stop "guessing" and start growing. They effectively freed up $65,000 in frozen capital which was immediately reinvested into a new product line.
Founder's Perspective
"At Microsoft, I saw how data can be a competitive weapon. For AeroGear, the goal wasn't just a prettier chart—it was about cash flow. By applying PhD-grade predictive logic to their Shopify data, we turned their warehouse from a cost center into a growth engine."
— Akhilesh Khope, PhD
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