Why a semantic layer is the difference between a 96% and an 80% accurate AI analyst
Why conversational analytics stalls and the data fundamentals that make it reliable.
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Inside dbt Wizard, the AI agent built to show its work
dbt Wizard's designer and engineering lead show how the agent validates its own work before code ever reaches production.
Showing our work: a controlled experiment in context engineering
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.
How Ramp's internal AI agents crossed a million sessions
Ramp shares the agents, sandboxes, and dashboards it built by treating internal AI as analytics engineering.
From mainframe to AI-powered maturity: Verisk's data modernization journey
Verisk modernized decades of insurance data, from mainframe to dbt, to get AI-ready.
From prompt to PR: how Careem’s AI writes and validates dbt models before a human ever sees them
Careem's AI agent writes and validates dbt models end to end before a human ever reviews the PR.
How a data scientist at Kilo built the business context layer his AI agents were missing
How a data scientist built an AI context layer for organizational knowledge a dbt project alone can't hold.
What a swarm of rogue AI agents can teach us about having better ideas
Benn Stancil uses the Hugging Face hack to explain why the most useful ideas come from the weirdest connections.