I’ve been building an internal AI-powered automation pipeline for Amazon/e-commerce listing generation and wanted to share some of the architecture + problems I solved while working on it.
The goal was simple:
Reduce the amount of manual effort required to generate optimized product listings at scale without sacrificing accuracy.
The pipeline currently handles:
- Product Titles
- Bullet Points
- Backend Search Terms
- Product Descriptions
- Bulk SKU Processing
- Keyword Integration from SQP/Search Data
A few technical problems that took the most work:
• Preventing hallucinations in product specs
One of the biggest issues with generic LLM workflows is incorrect dimensions, quantities, weights, pack counts, etc. I added validation/scoring layers to cross-check outputs against source catalog data before final generation.
• Preserving keyword quality without keyword stuffing
The system strategically injects high-volume search terms while still keeping listings readable and compliant.
• Scaling asynchronous batch runs
The backend processes large SKU batches asynchronously instead of generating listings one-by-one, which massively improved throughput for bigger catalogs.
Current stack:
- FastAPI backend
- React/Vite frontend
- Async processing pipeline
- Multi-agent workflow structure
- Structured export outputs for bulk uploads
One interesting edge case:
Even small formatting mistakes like changing “2.5kg pair” into “2.5 kg” or losing decimal precision can create major catalog inconsistencies, so I had to build safeguards specifically for that.
Curious how others here are approaching listing generation at scale:
- Fully manual?
- Hybrid AI workflows?
- Internal tooling?
- Agency pipelines?
Would genuinely love to hear what has or hasn’t worked for you.