Luxury Retail · CRM
Luxury Watch & Jewellery CRM
LaravelMySQLRESTful APIs
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Luxury Commerce · Watch Retail
Automating a 3-day manual image backlog down to under 5 minutes for a US watch retailer
A US-based B2B watch retailer sourcing from global auctions needed a reliable, fast pipeline to remove backgrounds, process product images, and deliver them to S3, without a manual bottleneck slowing down listings.
Client: US-based B2B luxury watch retailer
PythonNode.jsAWS S3React.jsBackground removal ML model
5 min
Photo to S3 (was 3 days)
80%
Cost reduction per image
1,500+
Images processed per week
0
Manual backlog remaining
The challenge
- 3-day backlog between photo and live listing: watches sat in inventory without generating revenue
- Inconsistent background removal quality across different operators
- Manual S3 upload process prone to errors and naming inconsistencies
- Staff time consumed by repetitive, low-value image processing tasks
- Listing delays directly impacted cash flow on acquired inventory
What we built
- Python ML worker detects the watch subject and removes background automatically
- Image resized, optimized, and formatted to product listing specifications
- Direct upload to AWS S3 with correct naming convention and folder structure
- React dashboard for queue monitoring, success/failure tracking, and manual override on edge cases
- Node.js orchestration manages the queue, retries failed jobs, handles batch processing
The results
| Before | After | Metric |
|---|---|---|
| 3 days | Under 5 minutes | Time from photo to S3 |
| Baseline | 80% lower | Processing cost per image |
| 3-day backlog | No backlog | Weekly 1,500+ images |
| Every image | Edge cases only | Manual intervention |
The pipeline eliminated the 3-day backlog entirely. Watches sourced on Monday are listed by Monday afternoon.
The outcome
The 80% cost reduction came from replacing per-image manual labor with automated processing. The React dashboard gives operators visibility without requiring them to touch every image.
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