Build a Context Layer to connect AI to your data
Live demo
November 24, 2026
11 a.m. EST
Virtual
Jon Lewis
Kevin Kim
What context engineering is and why Open Data Infrastructure depends on it.
How to build a governed system of record for metrics with the dbt Semantic Layer.
How to pull scattered context (docs, calls, wikis, warehouses) into governed, agent-ready tables with the Fivetran Context Layer.
How to organize content for retrieval and connect it to Claude, ChatGPT and your own agents with Agents Schema and MCP.
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Your teams already use Claude and ChatGPT. Those tools either can't reach your company data or reach it without your definitions. Your data team holds the definitions, the models and the controls, so it decides whether the business gets the right answer or a confident guess.
AI systems have no tribal knowledge. Unlike a new hire, they can't learn what "active customer" means from a hallway conversation. You have to state it, in a form they can retrieve and trust. Truly effective context engineering requires your team to control how context is built, tested, updated, and served. Building context on Open Data Infrastructure enables your teams to do this, instead of burying it in a tool’s proprietary interpretation of your sources. That practice is context engineering, and Open Data Infrastructure depends on it.
In 45 minutes, we’ll show you how to build a trustworthy context layer and connect it to the AI tools your teams already use.
This is the third webinar in a three-part series on Open Data Infrastructure as the foundation for AI at scale. Watch the first webinar here, and the second here.
What context engineering is and why Open Data Infrastructure depends on it.
How to build a governed system of record for metrics with the dbt Semantic Layer.
How to pull scattered context (docs, calls, wikis, warehouses) into governed, agent-ready tables with the Fivetran Context Layer.
How to organize content for retrieval and connect it to Claude, ChatGPT and your own agents with Agents Schema and MCP.
How to build the feedback loop that improves existing context and creates new context.
Jon Lewis
Kevin Kim
Jon Lewis
Kevin Kim
Your teams already use Claude and ChatGPT. Those tools either can't reach your company data or reach it without your definitions. Your data team holds the definitions, the models and the controls, so it decides whether the business gets the right answer or a confident guess.
AI systems have no tribal knowledge. Unlike a new hire, they can't learn what "active customer" means from a hallway conversation. You have to state it, in a form they can retrieve and trust. Truly effective context engineering requires your team to control how context is built, tested, updated, and served. Building context on Open Data Infrastructure enables your teams to do this, instead of burying it in a tool’s proprietary interpretation of your sources. That practice is context engineering, and Open Data Infrastructure depends on it.
In 45 minutes, we’ll show you how to build a trustworthy context layer and connect it to the AI tools your teams already use.
This is the third webinar in a three-part series on Open Data Infrastructure as the foundation for AI at scale. Watch the first webinar here, and the second here.