Case studyE-commerce
Optimizing Vision-Language Models for Product Cataloging
The challenge
A retail leader used large VLMs to catalog products—but models were slow, generic, and costly to fine-tune, creating deployment bottlenecks.
Key Obstacles
- Slow inference: Even quantized models lagged in production
- Poor specialization: Struggled with structured data extraction
- Complex deployment: Months of tuning needed for accuracy
Our solution
Liquid fine-tuned smaller, specialized VLMs for cataloging, using our Edge SDK to optimize both inference speed and accuracy.
The results
Faster, more accurate cataloging with 65% lower deployment time.
- 65% faster time-to-production
- Higher accuracy than larger generic models
- 50% lower compute/memory needs
- Seamless pipeline from fine-tuning to deployment