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AnthropicAnnouncement
Anthropic and NEC build AI engineering in Japan
Summary
NEC Corporation is partnering with Anthropic to build one of Japan’s largest AI-native engineering organizations and make Claude available to approximately 30,000 NEC Group employees worldwide. As Anthropic’s first Japan-based global partner, NEC will jointly develop secure, industry-specific AI products for finance, manufacturing, cybersecurity, and local government, while integrating Claude into Security Operations Center services and its next-generation cybersecurity service. Claude, including Claude Opus 4.7, and Claude Code will be incorporated into NEC BluStellar Scenario, initially supporting data-driven management and customer experience offerings. Internally, NEC will establish a Center of Excellence, use Anthropic’s technical enablement and training, and expand Claude Code and Claude Cowork through its Client Zero initiative. Deployment to employees is underway, and joint development has started.
Context
NEC is pursuing a strategic collaboration to expand AI use among its employees, develop secure AI products for Japanese customers, and build an AI-enabled engineering organization.
Approach / What changed
NEC will deploy Claude across its workforce, establish a Center of Excellence with Anthropic-supported training, integrate Claude products into NEC BluStellar Scenario and cybersecurity services, and use Claude Code and Claude Cowork internally through its Client Zero initiative.
Takeaways
Claude is being deployed to approximately 30,000 NEC Group employees worldwide, with Claude Code used by NEC’s planned AI-native engineering organization.
NEC and Anthropic are jointly developing secure, domain-specific products for finance, manufacturing, cybersecurity, and local government in Japan.
Claude Opus 4.7, Claude Code, and Claude Cowork will support NEC’s BluStellar offerings, engineering work, and internal business operations.
Source and freshness
- Source
- anthropic.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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