{"id":4447670,"date":"2026-06-01T20:13:17","date_gmt":"2026-06-01T18:13:17","guid":{"rendered":"https:\/\/www.deptagency.com\/case\/revolutionising-data-consumption-with-agentic-ai-mcp\/"},"modified":"2026-06-10T09:38:46","modified_gmt":"2026-06-10T07:38:46","slug":"revolutionising-data-consumption-with-agentic-ai-mcp","status":"publish","type":"case","link":"https:\/\/www.deptagency.com\/en-nl\/case\/revolutionising-data-consumption-with-agentic-ai-mcp\/","title":{"rendered":"Revolutionising data consumption with agentic AI & MCP"},"content":{"rendered":"
\n\tFinancial Services<\/h1>Revolutionising data consumption with agentic AI & MCP<\/p><\/div>\n\n\n\t\n\t\t\t\t\t\n\t\t\t\n\t<\/div>\n\n
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\n\t\t\t\n\t\t\t\t(  Services  )\n\t\t\t<\/p>\n\n\t\t\t\n\t\t\t\t\t\t\t\t\t
  • \n\t\t\t\t\t\tAI Transformation\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t
  • \n\t\t\t\t\t\tTech & Data\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t<\/ul>\n\t\t<\/div>\n\t\n\tA leading global financial services firm has partnered with DEPT\u00ae for several years to push the boundaries of digital innovation.<\/p>We first helped bring its AI strategy<\/a> to life by building a custom chat interface that made proprietary insights easier to access. Over time, that work became the foundation for a more ambitious AI strategy.

    Because we\u2019ve been embedded in the client\u2019s team for so long, the partnership is built on deep institutional knowledge, day-to-day collaboration, and trust. That has allowed our teams to take bigger swings in AI.

    As AI evolved, the firm saw an opportunity to make its proprietary research, market intelligence, and high-fidelity data available not just through a chat interface, but directly within the AI systems its clients were already using. Together with DEPT\u00ae, the organisation evolved from a standalone conversational assistant into an integrated agentic platform powered by the Model Context Protocol (MCP). The result is a new model for growth in which the firm\u2019s data products can be consumed by people, machines, agents, and enterprise AI ecosystems at scale.
    <\/p><\/div>\n\n\n\t\n\t\t\t\t\t\n\t\t\t\n\t<\/div>\n\n\n\tMoving beyond the chat interface<\/h2>\n\tThe research tool we helped build was an important first step. To protect accuracy, we implemented an LLM-as-a-judge evaluation framework for the initial research assistant \u2014 one of the first public solutions of its kind. A secondary LLM reviewed the primary AI\u2019s responses to help ensure that the outputs remained grounded in approved research data and within strict guardrails. It gave the client a safer way to explore AI while maintaining the trust and precision their brand depends on.

    As the AI landscape matured, the firm\u2019s enterprise clients moved quickly. They were building internal LLM-based systems and needed a direct way to integrate the firm\u2019s research into their workflows.

    That shift created a new challenge. Existing tools were still built for human interaction, requiring users to manually search, extract, and reformat information. At the same time, the firm had to navigate a major transition: moving from deterministic software into probabilistic AI while protecting accuracy, trust, and brand reputation.

    Because we were already deeply embedded in the business, we were able to help the firm look beyond incremental improvements and move toward a more ambitious AI-native model.<\/p><\/div>\n\t\t\t\t\t\n\t\t\t\n\t<\/div>\n\n\n\tFrom RAG to MCP <\/h2>\n\tWe partnered with the firm to reimagine how its teams build and deliver AI-powered products. Instead of siloed web experiences, the organization began shifting toward a more connected agentic ecosystem.

    Rather than continuing to invest only in a traditional retrieval-augmented generation, we pivoted toward a distributed network of MCP servers. This architecture exposes the firm\u2019s specialised tools through a standardised protocol, making its data and research available to AI clients such as Claude, Microsoft Copilot, and other enterprise systems.

    In practice, the firm\u2019s products are no longer locked inside a single interface. They can travel wherever clients need them.

    The solution also introduced strategic model orchestration to balance reasoning power, cost, and latency. Larger models handle more complex tasks, while smaller models support faster, more efficient requests. That creates a more scalable foundation for long-term growth.<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tA new standard for AI-powered data delivery<\/h2>\n\tThe shift from human-centric interfaces to machine-ready data delivery has opened up a new growth channel for the firm. Early feedback has been positive, especially around the ability to embed proprietary research directly into client workflows.

    Internally, the transformation has also created greater speed and flexibility. With AI-driven development tools and a more modular architecture, teams can move faster while still meeting the demands of a regulated financial services environment.

    The platform is now positioned to grow with the business, with internal squads contributing specialised tools to one cohesive experience.<\/p><\/div>\n\t\t\t\t\t\n\t\t\t\n\t<\/div>\n\n\n\tGrowth unlocked<\/h2>\n\tThis was not a one-off AI implementation. It was the next chapter in a multi-year partnership built on trust, embedded collaboration, and shared ambition.

    Through agentic AI and MCP, the organization can now deliver its most valuable research and data directly into the systems where clients work, build, and make decisions.

    The result is a faster, more flexible, and more future-ready model for growth\u2014one that positions the firm as a leader in AI-native financial intelligence.<\/p><\/div>\n\t\n\t<\/div>\n\n
    \n\tOn our mind<\/span><\/h2>\n\tAll insights<\/a>\n\t\n\t\t\n\t\t\t
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    \n\t\t\t\t<\/svg>\t\t\t<\/div>\n\n\t\t\t\n\t\t\t\tInsight\t\t\t<\/span>\n\t\t<\/div>\n\n\t\t\t\t\t
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