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Snowflake Automotive Berlin, Germany

Vay

Customer Service Automation · Real-time Analytics

replace a slow, resource-constrained data lake approach so telemetry, support, and operational data could be analyzed fast enough to improve urban remote-driving operations and customer support.

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12x Faster compute on 80 data sources
Over 300 Hours saved for customer service agents
migrated all of its data workloads into the AI Data Cloud in just three months

Reported impact

Business outcome

Vay achieved 12x faster compute across 80 data sources, saved customer service agents more than 300 hours through AI-assisted call analysis, and migrated all workloads to Snowflake in three months.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Vay uses Snowflake, Snowpipe, Amazon S3, and Cortex AI to analyze remote-driving operations and customer interactions across its urban mobility service. Telemetry and live video metadata from Remote Driving Stations, app events, CRM data, and customer support inputs land in Amazon S3 and are ingested into Snowflake through Snowpipe so diverse semi-structured sources can be queried from one engine. Teams use Snowflake’s compute and geospatial capabilities to explore roughly 80 data sources, track demand, and refine routes and vehicle operations in near real time. Vay also applies Cortex AI and AI_TRANSCRIBE to transcribe, summarize, and categorize support calls, helping customer service teams spot issues in the app or vehicles without manual review. The unified platform replaced an error-prone data lake approach and accelerated experimentation.

Data flow

Remote Driving Station telemetry, app activity, CRM data, and support-call artifacts land in Amazon S3 and are ingested into Snowflake through Snowpipe. Snowflake powers geospatial and operational analysis, while Cortex AI processes call audio and summaries for customer service and product-improvement workflows.

Solution architecture

4 components · 3 layers
  1. Ingest
    • Amazon S3 Holds upstream remote-driving and application data that feeds Vay’s Snowflake environment.
    • Snowpipe Loads diverse, evolving upstream data into Snowflake with schema-agnostic ingestion.
  2. Compute
    • Cortex AI Transcribes, summarizes, and categorizes customer calls so support teams can spot issues and trends faster.
  3. Storage
    • Snowflake Provides the central analytics engine for operational, geospatial, and customer-service analysis.

Architecture clues

  • Cortex AI classifies support calls and uses AI_TRANSCRIBE-based transcripts to identify trends.
  • Snowflake geospatial capabilities are used to track demand and follow vehicles in near real time.
  • The previous data lake suffered from out-of-resource errors and long query times.

Evidence from the source

12x Faster compute on 80 data sources
Today, the company uses Snowpipe to ingest data into Snowflake from an Amazon S3 storage environment
Using Snowflake’s AI_TRANSCRIBE feature, Vay can automatically transcribe audio files of its customer calls
saved customer service agents over 300 hours
Transparency Evidence & methodology 100/100 reference value · 79% 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 value100/100
  • 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 scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 evidence20/20
    4 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

    Vay customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/vay/
    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 Oct 7, 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 9, 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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