Agalaz vs mirrAR: Generative AI vs Traditional AR for Fashion Stores

mirrAR is a well-established multi-vertical AR brand with deployments in eyewear, beauty, jewellery and increasingly apparel. Agalaz is a newer generative AI try-on built specifically around the photorealistic-image-of-customer-wearing-the-product use case. The two products solve overlapping problems with different technology generations. This guide compares them honestly.

TL;DR

• Pick mirrAR if: brand marketing weight matters more than render fidelity, your catalogue is mostly eyewear/jewellery, and you have engineering capacity for the integration. • Pick Agalaz if: you want a believable photo-style result on clothing, swimwear, wedding dresses, suits or any non-face category, and a script-tag install instead of a custom integration.

The fundamental tech difference

mirrAR uses traditional AR overlays. Their stack tracks face or body landmarks via computer vision, then renders a 3D model on top of the camera feed in real time. The technology is mature and renders are sub-second. The newer mirrAR product layers some AI on top, but the foundation is overlay-based.

Agalaz uses generative AI rendering. Each try-on produces a fresh photorealistic image where the customer is wearing the actual product, with believable fabric drape, material realism, and body fit. Rendering takes ~10 seconds because the image is being generated, not overlaid.

For face-area products (sunglasses, lipstick), AR overlay is faster and the realism gap is small. For body-area products (clothing, swimwear, suits, dresses), generative AI looks dramatically more believable.

Side-by-side