AI inventory assistant that prevents stockouts & dead stock for small retail
Small retailers lose money to stockouts (missed sales) and dead stock (cash stuck on shelves). They don’t have forecasting tools like big chains — spreadsheets aren’t enough.
ShelfSense predicts what to reorder and when, using sales patterns, seasonality, and simple supplier data. It gives owners a clear “Do this today” action list.
Founder built a StartZig landing page targeting local groceries and boutique stores. Validation goal: “Will owners pay to avoid stockouts?”
Signal 27 signups in 48 hours from one community group + 6 store owners asked for a demo.
Defined ICP: stores with 300–5,000 SKUs and no in-house analytics. Revenue model: subscription ($49–$199/month) + optional onboarding.
Key Risk messy data. Solution: start with “top 50 products” and expand gradually.
Built a working prototype that simulates store sales uploads and generates reorder recommendations with confidence scores.
MVP Outcome owners preferred an “action list” over charts.
Pitched to StartZig’s virtual angels and raised $200,000 to expand product coverage and integrate POS exports.
This MVP shows what a store owner sees after uploading basic sales data.
“If this saves me even one stockout weekend, it pays for itself. The action list is perfect.”
— Grocery Owner (Virtual Tester)“I like the confidence score. I’d want a simple POS export integration and supplier lead times.”
— Retail Ops Advisor (Virtual Mentor)ShelfSense raised $200,000 from simulated angel investors on StartZig. The capital allocation:
ShelfSense demonstrates StartZig’s value: validate demand, build a plan, craft an MVP, and learn investor expectations — in a risk-free simulation.
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