How Rakuten uses Claude Code: enterprise case study
The number everyone quotes from Rakuten's Claude Code rollout is 99.9%. It is also the number that gets read wrong most often.
Here is what actually happened, according to Anthropic's published customer material. An ML engineer named Kenta Naruse asked Claude Code to implement an activation-vector extraction method inside vLLM, the open-source LLM serving library. The agent ran for seven hours. Naruse, in Anthropic's telling, "didn't write any code during those seven hours." The result was reported at 99.9% numerical accuracy against the reference method (Anthropic 2026a).
Read that as a productivity claim and you will build the wrong thing inside your own company. Read it as a test result and the case study becomes genuinely useful.
Because 99.9% is not evidence that the model is good. It is evidence that the task had an oracle. Somebody, before the agent started, already knew how to compute the right answer independently — a reference implementation existed, and the agent's output could be diffed against it. That single structural fact explains more about why Rakuten's rollout produced results than any statement about model capability you will find in the marketing.