How AlphaSense turned a 3-to-5-week data rebuild into a few hours with dbt and Euno's AI context

AlphaSense and Euno presented a case study showing how AI agents can work more effectively with enterprise data when supplied with continuously maintained, governed context rather than fragmented metadata assembled during a task. Euno builds a live context graph from data, ETL, BI, security, and observability systems, while AlphaSense uses dbt to govern models and definitions built from that context. In one example, AlphaSense rebuilt a legacy 287-field dashboard table with 68 upstream dbt models into a 13-model star schema in a few hours and roughly 15 prompts, reducing work previously estimated at three to five weeks while emphasizing testing and documentation. A second project used the system to produce a column-level billing architecture redesign and diagrams in about two hours without generating code, and a third ongoing effort aims to give conversational analytics agents a shared governed context. The presenters argued that this approach improves accuracy, speed, and user experience by making conflicting definitions explainable through live rules and by continuously evaluating agent performance with benchmark questions.

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