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Snowflake Financial Services Walsall, United Kingdom

RAC

Customer Service Automation

consolidate fragmented service data so agents could get faster context and make better roadside-assistance and customer-service decisions.

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2-3 days to turn insights into action – previously months
30% reduction in call handling times

Reported impact

Business outcome

The RAC cut call handling times by 30%, turned insights into action in two to three days instead of months, and created a single-pane agent experience that saves millions of pounds annually.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

RAC uses Snowflake and Cortex AI on Snowflake to consolidate fragmented service data so agents could get faster context and make better roadside-assistance and customer-service decisions. Roadside-assistance and customer-service data are consolidated in Snowflake so agents no longer need to piece together context from separate systems. MAVIS retrieves the relevant warehouse data through an API and uses Cortex AI to summarize what matters most for each customer interaction. Agents see the synthesized view in one interface, which speeds decisions, shortens calls, improves onboarding, and helps the RAC act on insights much faster than before. Keeping ingestion, storage, transformation, and governed access on one platform reduces handoffs, improves concurrency, and makes it easier to reuse trusted data across teams and applications. The RAC cut call handling times by 30%, turned insights into action in two to three days instead of months, and created a single-pane agent experience that...

Data flow

service and customer-interaction data flow into Snowflake and Cortex AI; curated data and model outputs are then served to contact-center and service agents.

Solution architecture

2 components · 2 layers
  1. Compute
    • Cortex AI Provides in-platform AI functions used for summarization, classification, generation, or model-assisted analysis.
  2. Storage
    • Snowflake Provides the centralized data platform that stores governed datasets and serves analytics or AI workloads.

Architecture clues

  • Built on a consolidated Snowflake data warehouse foundation.
  • Designed to improve onboarding and faster service decisions.
  • MAVIS is a single-pane application that calls Snowflake through an API and uses Cortex AI for summarization.

Evidence from the source

MAVIS pulls relevant information from Snowflake’s data warehouse in seconds through an API and summarizes it using Cortex AI
reduced call handling times by 30%
translating to millions of pounds of annual cost savings
Transparency Evidence & methodology 95/100 reference value · 74% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence74%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Snowflake

    RAC customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/rac/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Mar 23, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 11, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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