Rakuten Deploys AI Shopping Assistant Across E-Commerce

Rakuten, Japan's largest e-commerce conglomerate, has deployed a custom-built AI shopping assistant across all of its online retail services in Japan, marking one of the most ambitious integrations of generative AI into a live commerce platform to date. The move positions the company — often called 'the Amazon of Japan' — as a frontrunner in the race to embed intelligent agents directly into the shopping experience.

The AI assistant, built on Rakuten's proprietary large language model technology, is now live across Rakuten Ichiba, Rakuten Fashion, Rakuten Books, and several other verticals within the company's sprawling digital marketplace. It represents a significant escalation in how major e-commerce platforms worldwide are leveraging AI to reshape consumer behavior and boost conversion rates.

Key Facts at a Glance

• Full deployment across all Rakuten e-commerce verticals in Japan, serving over 100 million registered members
• The AI assistant is built on Rakuten's in-house LLM, not a third-party model like GPT-4 or Claude
• Features include personalized product recommendations, natural language search, purchase guidance, and post-sale support
• The system processes queries in Japanese and English, with additional language support planned
• Rakuten has invested an estimated $1 billion+ in AI development over the past 2 years
• The rollout follows a 6-month pilot program that reportedly increased average order value by 15%

Rakuten Bets Big on Proprietary AI Over Third-Party Models

Unlike competitors such as Shopify, which has integrated OpenAI's models into its platform, Rakuten chose to develop its AI capabilities in-house. The company's Rakuten AI division, established in 2023, has been quietly building a suite of large language models specifically optimized for commerce use cases.

This proprietary approach gives Rakuten full control over data privacy — a critical concern in Japan's regulatory environment — and allows the company to fine-tune models on its massive trove of transactional data. Rakuten processes approximately $40 billion in gross merchandise value annually, providing an enormous dataset for training commerce-specific AI.

The decision mirrors a broader trend among Asian tech giants. Companies like Baidu, Naver, and Alibaba have all invested heavily in building their own foundation models rather than relying on Western AI providers. For Rakuten, this strategy also serves as a competitive moat against Amazon Japan, which has been steadily gaining market share.

How the AI Shopping Assistant Actually Works

The assistant functions as a conversational commerce layer that sits on top of Rakuten's existing search and recommendation infrastructure. Users can interact with it through a chat interface embedded in the Rakuten mobile app and desktop site.

Key capabilities include:

• Natural language product search: Users describe what they want in plain language (e.g., 'a waterproof jacket for hiking in autumn under $200') and the AI returns curated results
• Comparison shopping: The assistant can compare products side-by-side, highlighting differences in price, ratings, materials, and shipping times
• Purchase history intelligence: It analyzes past orders to suggest replenishment items, complementary products, and size/fit recommendations
• Post-purchase support: The AI handles common customer service queries including order tracking, returns initiation, and warranty questions
• Merchant insights: Sellers on the platform receive AI-generated analytics about customer preferences and trending demand

The system goes beyond simple chatbot functionality. It maintains contextual memory within sessions, meaning users can refine their queries naturally without starting over. If a shopper asks for 'something cheaper,' the AI understands the context of the previous recommendation and adjusts accordingly.

Pilot Results Show Measurable Revenue Impact

Rakuten's 6-month pilot program, which ran across Rakuten Ichiba and Rakuten Fashion from late 2024 through early 2025, yielded compelling results. According to data shared by the company, users who engaged with the AI assistant demonstrated significantly different shopping behaviors compared to control groups.

Average order value increased by 15%, while session duration grew by 22%. Perhaps most importantly, return rates dropped by 8%, suggesting that AI-guided purchases resulted in better product-customer matches. The conversion rate for users interacting with the assistant was reportedly 3.2x higher than the platform average.

These numbers are particularly noteworthy when compared to early results from similar implementations. Shopify's Sidekick AI assistant and Amazon's Rufus have shown more modest improvements in their initial rollouts, though direct comparisons are difficult given differences in market dynamics and user demographics.

Industry Context: The AI Commerce Arms Race Intensifies

Rakuten's deployment arrives at a pivotal moment in the global e-commerce AI landscape. Every major platform is now racing to integrate generative AI into the shopping experience, but approaches vary dramatically.

Amazon launched its AI shopping assistant Rufus in early 2024, powered by a combination of proprietary and third-party models. The tool has seen mixed reviews, with critics noting that it sometimes generates inaccurate product information. eBay has integrated AI-powered image search and listing optimization. Alibaba has deployed its Tongyi Qianwen model across Taobao and Tmall, focusing heavily on merchant-side tools.

In the Western SaaS space, Shopify has been the most aggressive adopter, embedding AI across its merchant tools, from product description generation to customer service automation. The company reported that its AI features contributed to a measurable uplift in merchant satisfaction scores.

What sets Rakuten apart is the end-to-end nature of its deployment. Rather than offering AI as an optional feature or limiting it to specific use cases, the company has woven it into the fundamental shopping experience across every vertical. This all-in approach carries higher risk but could yield outsized rewards if execution is strong.

What This Means for the Global E-Commerce Landscape

Rakuten's move carries implications that extend far beyond Japan. For Western retailers and platform operators, it serves as a case study in how deeply AI can be integrated into commerce infrastructure.

Several key takeaways emerge for the industry:

• Proprietary models may offer advantages in specialized domains like commerce, where generic LLMs lack domain-specific training data
• Conversational commerce is moving from novelty to necessity — platforms without AI assistants risk falling behind in user experience
• Data privacy considerations are driving some companies to build in-house rather than share customer data with third-party AI providers
• Measurable ROI is achievable — Rakuten's pilot data suggests that AI shopping assistants can directly impact revenue metrics

For consumers, the deployment signals a shift toward more personalized, efficient shopping experiences. The days of scrolling through pages of search results may be numbered, replaced by AI-curated selections tailored to individual preferences and needs.

For merchants on Rakuten's platform, the AI layer introduces both opportunities and challenges. Sellers who optimize their product data for AI discovery will likely see increased visibility, while those with poor metadata may find themselves buried. This creates a new dimension of AI-era SEO that merchants will need to master.

Technical Architecture Reflects Lessons from AI Industry

While Rakuten has not disclosed the full technical specifications of its AI system, industry analysts believe the architecture follows a retrieval-augmented generation (RAG) approach. This combines the generative capabilities of LLMs with real-time retrieval from Rakuten's product database, ensuring that recommendations are grounded in actual inventory and pricing data.

The RAG approach addresses one of the most persistent challenges in AI commerce: hallucination. A shopping assistant that recommends products that don't exist or quotes incorrect prices would quickly erode user trust. By anchoring the LLM's outputs in verified product data, Rakuten minimizes this risk.

The system also reportedly uses a multi-model architecture, with different specialized models handling different tasks. A lightweight model manages simple queries and routing, while more powerful models handle complex comparison shopping and nuanced recommendations. This tiered approach helps manage computational costs — a critical consideration when serving over 100 million users.

Looking Ahead: Rakuten's AI Roadmap and Industry Implications

Rakuten has signaled that this deployment is just the beginning of its AI commerce strategy. The company plans to expand the assistant's capabilities to include voice commerce integration, allowing users to shop through smart speakers and connected devices. A visual search feature, where users can photograph items and find similar products on Rakuten, is expected by late 2025.

The company is also exploring cross-service AI integration across its broader ecosystem, which includes Rakuten Mobile, Rakuten Bank, and Rakuten Travel. Imagine an AI assistant that not only helps you buy a suitcase but also books your flight, arranges travel insurance, and handles currency exchange — all within the Rakuten ecosystem.

For the broader industry, Rakuten's full-scale deployment will serve as an important benchmark. If the revenue and engagement improvements seen in the pilot hold at scale, expect every major e-commerce platform to accelerate their own AI assistant programs. The competitive pressure is already mounting.

The message is clear: AI-powered shopping is no longer experimental. It's becoming the standard, and companies that fail to adapt risk losing ground to those that embrace it fully. Rakuten's bold move may well be remembered as a turning point in the evolution of online retail.