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Dun & Bradstreet SAME brings D&B’s global AI capabilities to local markets

Riyadh, Saudi Arabia: Dun & Bradstreet is expanding access to its verified commercial intelligence through collaborations with leading AI platforms, including Anthropic, AWS, OpenAI, Microsoft, Perplexity, Snowflake and others. Through Dun & Bradstreet South Asia, Middle East & Africa (D&B SAME), these capabilities are being made available to customers across the region, enabling organisations to…

Dun & Bradstreet SAME brings D&B’s global AI capabilities to local markets

Dun & Bradstreet is broadening its reach by integrating verified commercial intelligence into AI systems across South Asia, the Middle East, and Africa. The company has formed alliances with prominent AI platforms such as Anthropic, AWS, OpenAI, Microsoft, Perplexity, and Snowflake. These partnerships allow Dun & Bradstreet South Asia, Middle East & Africa (D&B SAME) to provide customers with access to its commercial insights directly within AI-powered applications, enterprise systems, and decision-making processes.

By connecting to the D&B Commercial Graph through secure Model Context Protocol (MCP) links and tailored platform integrations, businesses can effortlessly incorporate verified information about business identity, ownership, relationships, and risk into their operations. This move is aimed at enhancing operational efficiency and equipping organizations with more reliable data to inform their decisions.

Nauman Lakhani, Group Director of Products at D&B SAME, emphasized the importance of trusted commercial data in the rapidly evolving AI landscape, stating that it has become a critical foundation for effective decision-making. With the D&B Commercial Graph encompassing over 650 million business entities and backed by more than 100 billion monthly data quality checks, Dun & Bradstreet is empowering enterprises to develop AI-driven workflows that are firmly rooted in accurate and trustworthy information.

Written by urgent.news from Gulf News's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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