Dun & Bradstreet announced a collaboration with Amazon Web Services, Inc. (AWS) that brings the D&B Commercial Graph™ into Amazon Quick, an AI assistant for work, via Model Context Protocol (MCP). Users can now access D&B’s verified business information directly within Quick for a continuously validated view of business identity, ownership, and relationships to power AI-driven workflows in Quick across a range of sales, marketing and finance use cases.
Through natural language prompts, sales and marketing teams can identify and prioritize prospects, gain deeper insight into customers and buying organizations, uncover new opportunities and improve audience targeting. The same intelligence can also support finance, compliance and procurement teams as they assess risk and verify business relationships at scale.
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“We’re bringing our verified business data into Amazon Quick to meet our clients where they’re already working and building,” said Gary Kotovets, Chief Data Officer at Dun & Bradstreet. “By combining the D&B Commercial Graph with Quick, sales, marketing, and finance teams can use AI to generate more relevant insights, move faster and streamline key workflows.”
The D&B Commercial Graph connects hundreds of millions of business entities and relationships worldwide, helping organizations better understand customers, suppliers, partners and markets. Through MCP connectivity and a “headless” architecture, customers can access D&B’s Commercial Graph and related capabilities directly within the AI platforms, applications and environments they already use, without leaving their existing workflows.
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“Go-to-market teams need more than data to get returns on AI adoption. They need context,” Kotovets said. “Bringing D&B’s Commercial Graph into Amazon Quick makes it easier to identify the right prospects, better understand customers and buying organizations, and uncover opportunities for growth using AI.”
This integration into Amazon Quick continues a series of AI-driven advancements focused on making Dun & Bradstreet’s business information accessible within the environments where customers are building and deploying AI.
