We will present 3 AI systems for international e-commerce, deployed across AliExpress, Lazada, and supply chain. (1) Generative Recommendation at AliExpress — A generative recommendation system that reshapes traditional product discovery pipelines with generative AI techniques. It delivers substantial improvements in core business metrics, system compute utilization, provides highly adaptable support for diverse business requirements and unlocks richer shopping experiences. Attendees can interactively input queries categories to see real-time AI-generated personalized recommendations. (2) LazzieChat at Lazada — An AI chat assistant and intelligent shopping guide system integrating large models, agents, multimodal understanding, and e-commerce knowledge. Built around "user intent recognition" and "intent fulfillment," it covers product comparison, bundling, alternative recommendations, and list recommendations. It addresses long-tail queries through a Query Intent Rewrite Agent, Attendees will engage in multi-turn conversations, test visual search by uploading images, and experience localization features for Southeast Asian users. (3) Supply Chain Multi-Objective Multimodal Large Model with RLVR at AIDC — A technical presentation on a unified VLM architecture addressing "physical perception defocus" and "industry logic gap" in logistics tasks (weight estimation, dimension estimation, HSCode prediction, seasonality classification, logistics attributes, foldability discrimination). We will present the hybrid stratified resampling strategy, SFT + RLVR joint training, multi-dimensional dynamic weighted reward function, and GDPO algorithm that severs spurious causal chains. We will share experimental results showing significant improvements over Qwen closed-source model across all six tasks, and introduce the "Season Identification Agent" combining human-machine collaboration with temporal sales features.