Immersive, interactive experiences that actually convert

Models that convert images, audio, and text into a unified vector space. This enables cross-modal similarity search, fusion and retrieval (such as image-to-text or voice-to-product metadata). Without shared embeddings, modalities remain siloed and cannot power a unified experience. 

Fusion layers combine encoded signals using attention or multimodal transformers. This allows the system to reason across modalities, such as aligning visual context with textual descriptions and voice intent.

Large pre-trained models that process multiple modalities. These can be adapted into commerce flows for interactive assistants, content generation and multimodal question answering. 

Vector stores like FAISS or Pinecone index embeddings for fast nearest-neighbor search. This enables instant retrieval across modalities, such as matching a camera image to the product catalog.

Multimodal commerce requires immediate responses. Optimized GPU/TPU inference, quantized models and service orchestration ensure low latency for applications like visual search and shoppable video.

Pipelines that clean and prepare inputs from voice, cameras, video and AR sensors. This includes speech-to-text, image normalization, frame extraction and SLAM for AR experiences.

Generative models create adaptive product content: lifestyle imagery, generated video, PDP copy and more. These are typically built from multimodal foundation models. 

Persistent memory layers track user interactions across sessions and modalities. This enables tailored recommendations, adaptive flows and consistent AI behavior.

Object detection and video tracking make media interactive. Integrations with commerce APIs connect visual moments directly to the product catalog and checkout flow.

For AR, mobile, and in-store experiences, some processing must run on the device to maintain speed and privacy. This requires edge-optimized models, lightweight SDKs and deployment pipelines.