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Google Cloud Financial Services Australia

Fetch

Document Processing

Replacing manual data codification to solve the affordability dilemma

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Achieves 95%+ accuracy in veterinary coding with
At the heart of the infrastructure is Gemini in Vertex AI , which acts as the engine for analyzing messy clinical histories. Instead of a human agent reading 100 pages of notes, the AI ingests unstructured data—including handwritten vet…
Delivering total transparency upfront was once impossible due to the sheer volume of data, but the single-stack approach has made it viable. Fetch now achieves more than 95% accuracy in coding veterinary conditions, a level of precision…
Fetch set out to fix this by offering upfront certainty—being clear on what exactly is covered from day one. To do this, the team had to analyze a pet’s entire life story, which often meant sifting through more than 100 pages of…

Reported impact

Business outcome

Reduces 3-hour record reviews to minutes using Vertex AI; Achieves 95%+ accuracy in veterinary coding with; Operates on Google Cloud at a fraction of the costs

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Replacing manual data codification to solve the affordability dilemma At the heart of the infrastructure is Gemini in Vertex AI , which acts as the engine for analyzing messy clinical histories. Instead of a human agent reading 100 pages of notes, the AI ingests unstructured data—including handwritten vet records and… Key components include Gemini models in Vertex AI provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis; Cloud SQL stores transactional state and application data for the operational workflow at low latency; Cloud Storage stores large files, lake data, or static assets that feed downstream analytics and applications; Google Places API supplies location intelligence that is embedded into the user-facing workflow; Gemini models provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis. Reported outcomes include Reduces 3-hour record reviews to minutes.

Solution architecture

9 components · 3 layers
  1. Compute
    • Gemini models in Vertex AI Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Gemini models Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Vertex AI Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Cloud Run Runs serverless or virtualized application services with elastic scale for production AI workloads.
  2. Storage
    • Cloud SQL Stores transactional state and application data for the operational workflow at low latency.
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
  3. Serving
    • Google Places API Supplies location intelligence that is embedded into the user-facing workflow.
    • Firebase Provides the application backend and mobile services that connect customer-facing experiences to the platform.

Architecture clues

  • Fetch leverages a single Google Cloud stack to deliver transparent coverage and proactive health insights for healthier pets.
  • For many Australian pet owners, the true cost of veterinary care is a shock that comes too late. Advanced treatments for accidents or illnesses often exceed $10,000, a financial blow that is unmanageable for the 85% of…
  • Replacing manual data codification to solve the affordability dilemma
  • The Google AI Accelerator helped us redesign our portal so our vet-trained agents can work with maximum efficiency. By surfacing only the most relevant clinical data, we can scale our operations while maintaining the…
  • We realized that to truly change the industry and make pet care affordable, we couldn't just stitch together off-the-shelf software. We needed total control over our roadmap to solve complex data problems like analyzing…

Evidence from the source

Achieves 95%+ accuracy in veterinary coding with
At the heart of the infrastructure is Gemini in Vertex AI , which acts as the engine for analyzing messy clinical histories. Instead of a human agent reading 100 pages of notes, the AI ingests unstructured…
For many Australian pet owners, the true cost of veterinary care is a shock that comes too late. Advanced treatments for accidents or illnesses often exceed $10,000, a financial blow that is unmanageable for the 85% of…
Reduces 3-hour record reviews to minutes using Vertex AI
The Google AI Accelerator helped us redesign our portal so our vet-trained agents can work with maximum efficiency. By surfacing only the most relevant clinical data, we can scale our operations while maintaining the…
To avoid stitching together multiple vendors, Fetch built its entire sales, service, and data ecosystem on a single stack. This architecture allows the team to deploy custom logic that directly serves the customer,…
Transparency Evidence & methodology 87/100 reference value · 77% 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 value87/100
  • Problem clarity12/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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
    6 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 specificity8/10
    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. Google Cloud

    Fetch customer story

    First-party
    https://cloud.google.com/customers/fetch
    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 Apr 13, 2026 · 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 Mar 23, 2026 · Google Cloud · 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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