MCP in practice: Building repeatable AI workflows with custom MCP servers

Watch this workflow demo built for data scientists adding AI to workflows where the results have to hold up, whether to a reviewer, a security team, or a regulator. We start by diving into where MCP fits and why it offers more control than skills for reproducible workflows, then go deep on real world use cases for the life sciences industry.

From there, we show where that same pattern extends further. The use case comes from clinical research, but the architecture doesn't. Any workflow with a rules-based check against a standard, or free text mapped to a controlled taxonomy, is built the same way.

Beyond showing you how MCP servers work, our speakers walk you through the practical value they deliver: what it means when you need to reproduce a result, why it saves your team time, and what questions it lets you answer the next time someone asks how AI contributed to the analysis.

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