FashionAI by Samsung

Upload raw product images to the S3 bucket.

FashionAI triggers a webhook upon image detection.

Engine extracts attributes (color, pattern, fit).

Data is pushed directly into the client's PIM system via API.

Customer scans item barcode at the mirror.

Mirror displays the customer's digital avatar with the item.

System suggests matching accessories from the current inventory.

Customer requests physical sizes to be brought by staff via the mirror interface.

User uploads a 'street style' photo to the mobile app.

FashionAI identifies the key garment in the photo.

The engine queries the brand's catalog for visually similar items.

Results are ranked by similarity score and availability.

FashionAI monitors regional social media and local weather data.

System identifies a rising trend for 'linen' in a specific city.

Inventory levels are automatically adjusted in the regional warehouse.

Marketing ads are dynamically updated to feature linen products in that geo-fence.

System analyzes the user's past 12 months of purchase history.

FashionAI correlates style preferences with current weather and upcoming events on the user's calendar.

A 'Personal Shop' page is generated for the user.

Conversion rates are tracked and the model re-trains on successful clicks.

User provides height/weight or a body scan via smartphone.

FashionAI maps the user's dimensions to the garment's technical specs.

System provides a 'Fit Score' (e.g., 95% Match).

Returns are reduced by ensuring the user orders the correct size the first time.

Select a basic product photo.

FashionAI generates various lifestyle backgrounds using Stable Diffusion variants.

AI models with diverse body types are digitally 'dressed' in the item.

Multi-channel ad assets are exported in multiple aspect ratios.