Datassential, the trusted intelligence company for the food and beverage industry, announced the launch of its Model Context Protocol (MCP), a new distribution layer that allows teams to securely access Datassential’s proprietary insights directly within the AI tools they already use.
Often described as an “API for AI,” MCP enables AI systems to connect directly to Datassential’s industry-specific intelligence, turning natural language questions into accurate, Datassential-backed answers inside everyday AI workflows.
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“Our customers are already using AI every day to move faster and get answers,” said Jim Emling, CEO at Datassential. “What matters now is trust and the ability to get results you can rely on. With MCP, we’re connecting the AI tools they already use directly to Datassential’s high-quality intelligence, so teams get accurate, industry-backed answers without changing how they work or adding complexity.”
AI assistants such as ChatGPT, Claude, Copilot, as well as collaboration tools like Slack, are increasingly used across food and beverage organizations to support research, innovation, and decision-making. However, without access to trusted, industry-specific data, those tools often return generic or unverified answers that teams can’t confidently rely on.
MCP fills this gap by giving AI and collaboration tools secure access to Datassential’s curated, current food and beverage intelligence, so responses are grounded in trusted and proprietary data. Answers are traceable back to Datassential’s underlying datasets, giving teams confidence in where the insight comes from.
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MCP does not replace Datassential’s existing data shares or APIs, which continue to support data, analytics, and engineering teams. Instead, it expands access to the broader majority of users: the employees who will never write SQL or build integrations but still need fast, reliable food and beverage insights to do their jobs.
With MCP, users can ask a question and receive an answer in seconds inside their preferred tool, then link directly back to Datassential to validate findings or explore deeper analysis.