Ronja
Insights
Articles on data strategy, analytics, and making smarter decisions with your business data.
14 articles · Updated April 2026
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Frequently asked questions
What does Ronja actually do?
Ronja is the control plane through which AI agents access enterprise data – securely, accurately, and without driving up warehouse costs. It connects to your data warehouse, data lake, and 100+ SaaS apps, brings all of it under one governed layer, and lets anyone in the organization get answers through Slack, ChatGPT, Claude, or Ronja directly. Every number traces to its source: real code, real rows, real lineage. No hallucinations.
How is Ronja different from a traditional BI tool?
Traditional BI tools like Tableau or Power BI require analysts to build dashboards that business users then consume. Ronja removes the middle step: business users ask questions in plain language and get governed, accurate answers instantly. Ronja also layers on top of your existing stack – the warehouse stays, dbt stays, existing dashboards stay. It handles the ad-hoc questions that currently sit in the data team's backlog.
Does Ronja replace our data team?
No. Ronja is the data team's first line of support. It handles routine ad-hoc questions so the data team can focus on the hard problems that require human judgment. As more users interact with Ronja, it surfaces what the business actually needs: data gaps, divergent definitions, and common patterns. The data team goes from reactive firefighting to proactive, strategic work.
What about warehouse costs when everyone starts querying data?
This is one of the three core obstacles Ronja solves. Ad-hoc queries run on Ronja's own execution layer – they never hit the warehouse. Adding more users does not increase your warehouse bill. Your warehouse continues to handle what it was designed for: heavy transformations, scheduled pipelines, and system-of-record storage.
How does Ronja ensure answers are accurate and consistent?
Ronja maintains a federated context layer that aggregates definitions from wherever they already live: dbt models, Cube, Confluence, warehouse metadata, and user conversations. It detects where definitions diverge, learns from every correction, and ensures the same question always gets the same answer, regardless of who asks or which channel they use. Every answer traces to source – real code, real rows, real lineage.