Picture this: your company sits on tens of petabytes of data. To put that into perspective, if I had a penny for each byte and stacked them up, I'd have enough to reach Pluto and back, with some change left over.

That’s the reality we face at Rocket Mortgage, and it's probably not too different from what your organization is facing.

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All that data represents a goldmine of insights, but the challenge has always been making it accessible to the people who need it most (executives, analysts, and decision-makers who understand the business but don’t necessarily code SQL in their sleep.)

That’s why we built Rocket Analytics, and today I want to take you behind the scenes of how we created a text-to-SQL application using agentic RAG (Retrieval-Augmented Generation).

This tool fundamentally changes how our teams interact with data, letting them focus on what they do best: asking strategic and thoughtful questions, while the system handles the technical heavy lifting.

What Rocket Analytics actually does

Here’s how it works in practice: a user asks a natural language question, such as:

“Give me the count of loans for the past six months.”

Behind the scenes, the system:

  1. Converts the question into a SQL query
  2. Executes it against the relevant database
  3. Returns the results in a clean, understandable format
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But the real magic comes when users ask follow-up questions. They can request insights based on the initial results, or generate dashboards highlighting actionable patterns and trends.

During a recent demo, someone went from raw loan counts to a comprehensive dashboard showing:

  • Total loans closed
  • Average daily loans
  • Maximum daily loan dates
  • Trend analysis

—all within seconds. For executives and stakeholders in the mortgage industry, where speed of decision-making is crucial, this capability is transformative.