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Unlocking the power of data: How we built text-to-SQL with agentic RAG at Rocket Mortgage

ByRicardo
Unlocking the power of data: How we built text-to-SQL with agentic RAG at Rocket Mortgage
Unlocking the power of data: How we built text-to-SQL with agentic RAG at Rocket Mortgage

Picture this: your organization sits on tens of petabytes of information. To put that into perspective, if I had a penny for every byte and stacked them up, I’d have sufficient to achieve Pluto and again, with some change left over.

That’s the actuality we face at Rocket Mortgage, and it is in all probability not too totally different from what your group is dealing with.

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All that information represents a goldmine of insights, however the problem has at all times been making it accessible to the individuals who want it most (executives, analysts, and decision-makers who perceive the enterprise however don’t essentially code SQL of their sleep.)

That’s why we built Rocket Analytics, and at this time I wish to take you behind the scenes of how we created a text-to-SQL software utilizing agentic RAG (Retrieval-Augmented Generation).

This software basically adjustments how our groups work together with information, letting them give attention to what they do finest: asking strategic and considerate questions, whereas the system handles the technical heavy lifting.

What Rocket Analytics truly does

Here’s the way it works in observe: a person asks a pure language query, reminiscent of:

“Give me the depend of loans for the previous six months.”

Behind the scenes, the system:

  1. Converts the query right into a SQL question
  2. Executes it in opposition to the related database
  3. Returns the ends in a clear, comprehensible format
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But the actual magic comes when customers ask follow-up questions. They can request insights primarily based on the preliminary outcomes, or generate dashboards highlighting actionable patterns and traits.

During a current demo, somebody went from uncooked mortgage counts to a complete dashboard exhibiting:

  • Total loans closed
  • Average every day loans
  • Maximum every day mortgage dates
  • Trend evaluation

—all inside seconds. For executives and stakeholders in the mortgage trade, the place velocity of decision-making is essential, this functionality is transformative.

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