Scanly

Bulk wholesale catalog analysis that surfaces high-margin, in-demand products.

100k/hr

UPCs scanned per hour

99.8%

Fee accuracy

• CLIENT

Volume wholesale buyers and distributor teams needed to rapidly evaluate distributor price lists containing tens of thousands of SKUs to uncover hidden margins before inventory sells out.

• OBJECTIVE

Create a high-speed catalog processing platform that analyzes vendor price lists in bulk and ranks profitable sourcing opportunities instantly.

Location:🇺🇸 USA

Development time:3 MONTHS

Cooperation period:ACTIVE CONTRACT

Services used

Data & AnalyticsE-Commerce ToolingPythonVue.js

Project Team

LEAD DATA ENGINEER • ALGORITHM SPECIALIST • VUE.JS DEVELOPER • QA ENGINEER

Work Approach

Distributed parallel matching

Comprehensive fee calculation engine

Distributed parallel matching

Implemented asynchronous worker pools to query live marketplace pricing, sales rank, and historical trends for 100,000+ UPCs per hour.

Comprehensive fee calculation engine

Accurately calculated dimensional weight, FBA pick/pack fees, referral percentages, and estimated monthly sales velocity.

Technical Architecture

High-Throughput Scanner

Backend: Python with Celery and Redis

Database: PostgreSQL with optimized indexing

Frontend: Reactive Vue.js data grid with custom filtering

Results

Sourcing Efficiency Scaled 10x

Buyers evaluate supplier lists in minutes instead of days, securing top-margin inventory before competitor buyers can react.

100k/hr

UPCs scanned per hour

99.8%

Fee accuracy

10x

Faster sourcing decisions

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