Post by Plotono

A data team told us recently that their company had done everything right. They invested in a modern data stack — a powerful warehouse, scalable pipelines, dashboards across every team, and a growing data catalog. From a technical perspective, the platform was solid. Yet every single day, the same questions appeared in Slack: “Where is the churn dataset?” “Which table contains revenue by region?” “What does this metric actually mean?” Not because the data was missing. But because the knowledge about the data was hard to access. The platform stored data perfectly. But understanding it still required asking the data team. And this isn’t a rare situation. Even the best data platforms eventually accumulate thousands of tables, pipelines, and metrics. Documentation becomes fragmented. Metric definitions evolve. Tribal knowledge lives in analysts’ heads. So people do what humans always do when systems become complex: They ask someone. This is exactly the problem we’re trying to solve at Plotono. Instead of forcing users to navigate catalogs, documentation, and dashboards, our AI assistant uses Retrieval-Augmented Generation (RAG) to connect directly to the platform’s knowledge layer. So when someone asks: • Where is the churn dataset? • How is this metric calculated? • Which pipeline produces this table? • Can you generate a SQL query for this analysis? The assistant retrieves the relevant schemas, documentation, and pipeline metadata — and generates answers grounded in that context. The goal isn’t to replace the data platform. It’s to make the platform understandable. Curious to hear from others building AI features in data platforms: Are you experimenting with RAG for data discovery, SQL generation, or metric understanding?