High-end spirits retailer solves inventory chaos, cuts costs with scalable desktop app solution

Our client is the leading e-commerce platform for rare, luxury, and ultra-premium spirits, offering personalized gifting and a seamless customer experience. Powered by advanced technology and a nationwide retail network, the company simplifies compliant alcohol purchases on its site and across partner platforms, delivering curated, limited-edition offerings to modern, convenience-driven consumers.

The company faced a major data integration hurdle. As the platform aggregated inventory from a wide network of licensed liquor stores, it encountered significant inconsistencies in product data.

Each store used different point-of-sale (POS) systems, and even within the same software, data entry conventions varied dramatically. Something as simple as a bottle could be labeled “BOTTLE,” “BOT,” or just “B”—and that was just the beginning. These discrepancies made it extremely difficult to normalize product listings and accurately match them to the centralized online catalog. The result was a time-consuming, error-prone process that strained internal resources and threatened the user experience.

We developed a desktop application using Electron, backed by a Node.js and MongoDB backend, that enabled store staff to actively participate in the product matching process.

The system automatically surfaced likely catalog matches for each inventory item, flagging inconsistencies and prompting users to confirm or correct them. This semi-automated workflow effectively addressed the long tail of mismatches that the algorithm alone couldn’t resolve, particularly in edge cases where inconsistent naming or packaging made automated classification unreliable.

By shifting part of the validation effort to end users—through an intuitive desktop interface—the platform significantly improved match accuracy while reducing the internal burden of manual data cleanup.

Most inventory is now matched automatically in real time, with human oversight efficiently resolving exceptions. This dual approach increases match rates, minimizes manual intervention, and ensures the company’s digital shelves reliably reflect in-store stock across its nationwide network.

Today, the company operates with cleaner, more reliable inventory data and a scalable matching workflow that cuts costs, boosts accuracy, and ensures its digital shelves always reflect the premium experience customers expect.