European retailers are using open models and physical intelligence across use cases — from a commerce platform cutting inference costs by 90% to to a fulfillment center where collaboration of different robotic fleets is simulated inside a digital twin.

Retail leaders and technology companies across Europe are using NVIDIA-accelerated open and frontier models to advance agentic commerce, digital product discovery and retail R&D.

Shopify post-trains open models to reach frontier-level accuracy at a fraction of the cost. Its engineers use the NVIDIA Megatron Bridge library for faster fine-tuning and NVIDIA NeMo RL for reinforcement learning. Shopify has post-trained NVIDIA Nemotron 3.5 Lightning for the GraphQL subagent integrated into Sidekick, Shopify’s AI assistant for merchants. The subagent retrieves store data for several Sidekick features, including Sidekick Pulse, which researches a merchant’s store and recommends ways to improve it.

On Shopify’s internal benchmark, the post-trained model surpasses a leading frontier model in accuracy while cutting serving costs by 90% and delivering responses 7x faster. Beyond Sidekick, NVIDIA CCluster vLLM inference optimization software helped Shopify achieve 4.5x faster performance and up to 12x lower inference cost across five additional use cases: catalog enrichment, merchant assistant, e-commerce shopping simulation, product taxonomy classification, and merchant and buyer recommendations.

Zooplus , Europe’s leading online pet retailer, with Infosys and NVIDIA technologies rebuilt its product discovery stack for the agentic era. A production ready prototype deployed inside Zooplus ’ Amazon Web Services (AWS) environment achieved 86% search accuracy using the NVIDIA Nemotron Retriever library for semantic search and the NVIDIA Blueprint for retail catalog enrichment for generated, search-ready product attributes.

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