## 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