Enterprise System

Rakuten's Autonomous Claude Code Refactoring Work

Rakuten's public case study is useful because it attaches concrete numbers to Claude Code adoption: time-to-market reduction, sustained autonomous coding duration, and complex modification accuracy.

Updated 2026-07-06 8 min Rakuten Claude Code autonomous coding
Anthropic Customer Stories79%time-to-market reductionRakuten reports moving from 24 working days to 5 days for new-feature time to market.Anthropic Customer Stories7hsustained autonomous codingThe case study reports seven hours on a complex open-source refactoring project.Anthropic Customer Stories99.9%code modification accuracyThe case study reports 99.9% accuracy on complex code modifications.

"not just automating existing tasks"

Yusuke Kaji, Rakuten - source

What was built

Rakuten reports using Claude Code to transform software development across a large technology organization. The public case study names autonomous coding on complex refactoring work, faster new-feature delivery, high-accuracy code modifications, and managed-agent deployment across product, sales, marketing, and finance.

The case is broader than developer tooling alone. Rakuten also describes managed agents that plug into Slack and Teams so employees can assign tasks and get deliverables such as spreadsheets, slides, and apps.

The build pattern

The Rakuten pattern is scale-first adoption. The company frames Claude Code around speed, ROI, quality, security, and customer impact rather than isolated personal productivity.

For builders, the key lesson is to define success around delivery throughput and quality metrics. Rakuten reports time-to-market reduction and modification accuracy, which are more meaningful than screenshots of generated code.

  • Measurement: feature time to market, autonomous work duration, code modification accuracy.
  • Surface: Claude Code for engineering plus managed agents across business teams.
  • Governance need: enterprise-grade security and quality standards are explicit constraints.

Evidence and limits

The numbers are reported in an Anthropic customer story, not in a public benchmark repository. That means the page treats them as source-backed claims from an official case study, not independent measurements.

The project still belongs in the gallery because it is one of the clearest public enterprise examples of Claude Code attached to delivery metrics rather than only developer sentiment.

Primary Sources

Source trail

Links are cited for factual claims on this page. Access dates are kept so future updates can re-check drift.