finance_ops · finance · workflow
NYSE/ICE builds a structured RAG text-to-SQL system on Databricks Mosaic AI with 96% execution accuracy in five weeks
ICE business users had no way to query structured financial data without understanding data models, SQL schemas, or writing SQL queries, creating a barrier to self-service analytics.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits natural language question
When a question is submitted, an embedding vector is created and matched against the vector indexes.
Tools used
Unity CatalogVector SearchFoundation Model APIsModel ServingInference TablesAgent Bricks AI GatewayLlama3.1-70BMLflowGitHubSpider
Outcome
In five weeks, the team built a text-to-SQL system achieving 77% syntactic accuracy and 96% execution matches, enabling non-technical business users to query structured financial data in natural language.
Results
Time savedfive weeks
Volume77%
Grounding & classification
Source type: technical build writeup
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