Financial services · Snowflake
The RAC Fuels AI Ambitions, Reaches Insights Faster By Consolidating Data
Public case study · not a Metric Oak engagement
- reduction in call handling times
- 30%
- days to turn insights into action – previously months
- 2-3
What happened
The story in brief
A long-established UK roadside assistance and motoring services provider had data spread across many systems. Consolidating onto Snowflake simplified its data strategy, shortened time to insight and laid the groundwork for AI use cases.
See the full financial services benchmarkPlanning something similar?
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Governed, retrievable data for GenAI, RAG and agents. Senior engineers, fixed scope, measured results.
Frequently asked questions
What results did The RAC report?
The RAC reported 30% reduction in call handling times; 2-3 days to turn insights into action – previously months, according to its customer story published by Snowflake.
Which platform and technologies did The RAC use?
The RAC used Snowflake on Azure, including Apache Spark, Snowpark, Cortex AI, Secure Data Sharing, Snowflake Marketplace, Large language models.
How do these results compare with other financial services programs?
See our financial services benchmark for median cost reductions and speed-ups across published results in the industry.
Source
Customer story published by Snowflake: The RAC Fuels AI Ambitions, Reaches Insights Faster By Consolidating Data (opens snowflake.com in a new tab) on snowflake.com. Figures as published, retrieved 27 October 2026. The summary above is Metric Oak's own. This is not a Metric Oak engagement.
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