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Anthropic opens Singapore office and signs government AI MOU

Anthropic opened its fifth Asia-Pacific office in Singapore and signed an MOU with the country's digital ministry on AI safety, evaluation, adoption research and skills.

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Oct 14, 2026 · 1 min read

Anthropic opened an office in Singapore and signed a memorandum of understanding with the country’s Ministry of Digital Development and Information, creating a formal channel for work on AI safety, model evaluation and adoption.

The MOU covers three areas: AI safety, security and model evaluation; research into AI adoption and its impact; and responsible adoption across the public and private sectors. It provides a framework for future cooperation, not a record of completed programs or outcomes.

Under the framework described by Anthropic, the parties plan technical exchanges with Singapore safety, security and assurance institutions on model evaluation, testing standards and incident response. Anthropic said it would also explore AI-skills programs, support for small and medium-sized businesses, capability building for public officers, research and data sharing, and technical and policy advice.

Anthropic co-founder Ben Mann and ministry Permanent Secretary Chng Kai Fong signed the MOU. Digital Development and Information Minister Josephine Teo and Anthropic’s Asia-Pacific policy head Michael Sellitto witnessed the signing.

Anthropic described Singapore as its fifth office in the Asia-Pacific region. The opening gives the company a corporate presence in a market where in-region Claude 5 inference in Singapore is available through Amazon Bedrock.

The agreement follows earlier evaluation work between Anthropic and Singapore’s Infocomm Media Development Authority. In 2023, IMDA named Anthropic as a participant in its Generative AI Evaluation Sandbox and described a joint red-teaming project focused on Singapore’s multilingual and cultural context. In 2024, IMDA said it was working with Anthropic on a practical guide to multilingual and multicultural red teaming for large language models.

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