Rakuten
AI development for Rakuten Institute of Technology
Rakuten is a global leader in e-commerce and digital services, serving millions of customers across retail, finance and technology. Its research division, the Rakuten Institute of Technology, pioneers innovations that improve customer experience and advance AI-driven solutions for the global marketplace.
The problem
With one of the world's largest product catalogs and a rapidly expanding user base, Rakuten needed AI systems to unlock insights from massive datasets and deliver more personalised commerce.
- 01Forecast sales accurately across an enormous, volatile catalog
- 02Segment buyers in real time for personalisation
- 03Give merchants actionable insight into buyer behaviour
- 04Meet enterprise AI security and global compliance requirements
- 05Turn a massive, high-velocity product catalog into usable model features
- 06Give merchants tools that translate model output into action
What we did
Data & modelling foundation
Pipelines and feature engineering over large-scale commerce data as the base for reliable models.
Sales forecasting models
Machine learning for demand prediction, reducing stockouts and overstock.
Real-time segmentation
Buyer segmentation surfaced to merchants for targeted campaigns.
AI security & compliance
Global regulatory adherence built into the system to maintain customer trust.
Customer-centric recommendations
Enabling merchants to deliver more relevant offers and improve engagement.
Iterative model evaluation
Ongoing testing and refinement of forecasting and segmentation models against live data.
What shipped
Demand forecasting
Predictive models improving planning accuracy across markets.
Buyer segmentation
Real-time cohorts merchants can act on immediately.
Recommendation signals
Relevance scoring powering more targeted offers.
Analytics tooling
Dashboards translating model output into merchant decisions.
Scalable AI framework
A reusable foundation for ongoing experimentation.
Compliance tooling
Guardrails keeping models aligned with global regulatory requirements.
- Improved sales forecast accuracy, enabling better planning
- Optimised inventory management and supply chain efficiency across global markets
- Enhanced customer segmentation with deeper buyer-behaviour insight
- Increased personalisation, boosting satisfaction and retention
- Future-proofed, scalable AI infrastructure
- A reusable modelling foundation for future experimentation
- →AI development and analytics for large-scale retail platforms
- →Real-time buyer segmentation tools for personalisation
- →Machine learning for sales forecasting in global marketplaces
- →Enterprise AI security and compliance at scale
- →Feature engineering pipelines built for very large, fast-moving catalogs
- →Analytics tooling that turns model output into merchant action
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