RAG Model Workflows for Data Analysis

An ML lead evaluated 31 models across 6 providers for a 300,000-record bulk text-structuring workload to determine the most cost-effective rag implementation. The dashboard features KPI cards and a log-scaled scatter plot mapping blended batch cost against median latency, revealing a 450x spend range from $9.00 to $4,000. By identifying llama-3.1-8b as the frontier model with a 180 ms latency and flagging 19 models for misleading pricing, the engineering team secured procurement sign-off based on a defensible cost-latency tradeoff.

What it shows:

Visualizing blended batch costs against median latency enables engineering teams to secure procurement sign-off for frontier models.

A banking sector financial analyst used CambioML to process raw nested JSON regulatory exports into a Q2 2024 financial concentration study of the top 250 U.S. banks. The dashboard visualizes $21.2 trillion in total industry assets, highlighting that the top 10 banks control a 59.29% asset share and a 61.88% income share. By plotting an asset concentration curve and identifying profitability outliers like Comenity Bank and Flagstar Bank National Assn, the analyst demonstrated how tracking vector database updates can streamline complex regulatory data transformations without manual coding.

What it shows:

Processing raw nested JSON regulatory exports reveals critical asset concentration metrics and profitability outliers across the banking sector.