ShelfSense — AI-Powered Inventory Intelligence for E-Commerce
An internal AI tool that cut overstock waste by 35% in the first quarter.
A fast-growing e-commerce company with 4,000+ SKUs was struggling with inventory forecasting. Their buying team relied on gut instinct and lagging sales reports, resulting in chronic overstocking of slow movers and stockouts on trending items. They wanted an AI-powered internal tool that could analyse historical sales data, seasonal patterns, and supplier lead times to generate actionable reorder recommendations — something production-grade that their operations team could trust, not just a data-science demo.
We developed ShelfSense as an AI-powered web tool using Next.js for the interface and a Python-backed inference service calling Claude API for natural-language demand analysis and structured reorder recommendations. The system ingests sales history, supplier catalogues, and marketing calendars, then produces weekly buy-lists ranked by confidence score. We integrated it directly into their existing admin portal so the buying team did not need to learn a new tool. The build ran as a fixed-price SaaS development engagement over ten weeks with weekly demos, giving the client full visibility at every milestone.
Impact &
Outcomes
35% reduction in overstock waste within the first quarter
18% improvement in in-stock rate on top-selling SKUs
Buying team adoption at 100% — replaced the old spreadsheet workflow entirely
We had been burned by an AI proof-of-concept that never made it to production. HopeSols built something our team actually uses every Monday morning. The direct-access model meant we could shape the tool to fit our real workflow, not a generic demo.
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