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Catalog Tag Validator

Upload a product image and supplier-provided tags (color, material, category, style). LFM2.5-VL analyzes the image and flags which tags are correct, which need correction, and which are mismatched. Includes a batch scan simulator showing cost at catalog scale vs GPT-4o Vision.

Vision + language understanding — LFM2.5-VL sees the product image and validates each supplier tag against what it actually observes — color, material, style, category

10x cheaper at scale — Self-hosted at $0.0003/image vs GPT-4o at $0.003/image. At 20M products: $6K vs $60K

No data leaves your infrastructure — Product images and catalog data stay on your GPUs. No cloud API round-trips, no data residency concerns

The Problem

Suppliers submit millions of product listings with incorrect tags — wrong colors, materials, and categories. Manual QA catches a fraction. Cloud vision APIs like GPT-4o cost $60K per 20M images.

How LFM Compares

Manual spot-checking misses the majority. Cloud vision APIs are accurate but at $0.003/image, scanning a full catalog is prohibitively expensive. Rule-based checks cannot verify visual attributes.

What LFM Unlocks

Visual tag validation at 10x lower cost than GPT-4o Vision. Self-hosted on your infrastructure — no data leaves your perimeter. Process 20M product images for $6K instead of $60K.

Drop an image here or click to upload

PNG, JPG up to 10MB

Or try a sample:

This demo is fine-tuned on sample data. Results improve with your data.

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