This blog post provides a comprehensive guide on building a smart Elasticsearch agent that facilitates natural language querying of data, eliminating the need for users to master Elasticsearch's complex DSL. The agent's workflow, orchestrated by LangGraph and powered by Google's Gemini model for natural language understanding and query generation, involves normalizing user queries, translating them into Elasticsearch DSL, executing the search, and summarizing the results for

Marketing leaders in financial services are grappling with a significant challenge. However, a shift to Looker, especially when built on BigQuery and supported by a robust semantic layer, is transforming these frustrations into progress. The result? Significant impacts, from reduced dashboard maintenance to uncovering hidden attribution gaps, and most importantly, CMOs shifting from defending budgets to owning their results.

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