Automating Warehouse Inventory with Computer Vision

The Enterprise Challenge

As the client's distribution network expanded across Maharashtra, their physical inventory management became a critical liability. They were managing tens of thousands of discrete auto parts, but their tracking system relied entirely on manual human auditing.

The Operational Bottleneck

Every quarter, the company was forced to halt fulfillment operations for three full days to conduct a manual stock count. Warehouse workers physically counted boxes and parts, leading to severe human error. These miscounts resulted in inaccurate data within their central ERP, causing the sales team to accidentally sell out-of-stock items, which damaged client relationships and incurred SLA (Service Level Agreement) penalties.

The Foxkrit Architecture

We engineered a bespoke Computer Vision (CV) application designed specifically for the chaotic environment of a warehouse. Rather than overhauling their hardware, we deployed the AI model to run on standard rugged tablets. We trained the model to recognize their specific packaging, part numbers, and barcode variations.

Now, instead of manually counting, warehouse staff simply pan the tablet's camera across an aisle. The AI instantly detects, counts, and categorizes the inventory on the shelves via edge computing. We then built a secure API bridge that pushes these verified counts directly into their central database in real-time, completely bypassing manual data entry.