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Google Cloud Software / SaaS Israel

Wonderful

Customer Service Automation · Document Processing · IT Operations Automation

Wonderful was looking for a technology partner that could combine advanced multilingual models with globally distributed infrastructure, at a cost that scaled with their growth. They also needed strong reliability, trusted security, and excellent large-context performance. For this, they turned to Google Cloud . "Our journey with Google Cloud began with and Vertex AI ," Winkler says.

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Gemini powers expansion into 30 multilingual markets
Helps companies cut customer service costs by up to 70%
powers expansion into 30 multilingual markets

Reported impact

Business outcome

Gemini; powers expansion into 30 multilingual markets; Enables deployment of AI agents in days, not months; Helps companies cut customer service costs by up to 70%.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Wonderful uses Vertex AI to support model orchestration, while Gemini forms a crucial part of the solution's real-time voice pipeline, which stitches together multiple speech and reasoning models to produce human-like interactions across dozens of languages. A Zero Data Retention (ZDR) configuration ensures that all data processed is handled in-memory only. The company has expanded to a full Google Cloud deployment, leveraging Compute Engine Cloud Storage Google Kubernetes Engine (GKE) to manage its operations. Wonderful was looking for a technology partner that could combine advanced multilingual models with globally distributed infrastructure, at a cost that scaled with their growth. They also needed strong reliability, trusted security, and excellent large-context performance. For this, they turned to Google Cloud . "Our journey with Google Cloud began with and Vertex AI ," Winkler says.

Data flow

stored or unified in Cloud Storage, processed with Vertex AI, orchestrated via Google Kubernetes Engine (GKE), and surfaced through Gemini

Solution architecture

4 components · 4 layers
  1. Compute
    • Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
  2. Storage
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
  3. Serving
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
  4. Orchestration
    • Google Kubernetes Engine (GKE) Orchestrates the platform's containerized services and high-concurrency workloads.

Architecture clues

  • # Meet the startup bridging the AI transformation gap across markets with Gemini
  • Gemini
  • Gemini powers expansion into 30 multilingual markets
  • Helps companies cut customer service costs by up to 70%
  • Our journey with Google Cloud began with Gemini and Vertex AI. AI quality was our top priority, and Gemini consistently delivered.
  • Powering real-time, multilingual intelligence with Gemini

Evidence from the source

Enables deployment of AI agents in days, not months
Extracts key answers from huge knowledge bases in realtime
Gemini
Helps companies cut customer service costs by up to 70%
powers expansion into 30 multilingual markets
Transparency Evidence & methodology 95/100 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 value95/100
  • Problem clarity20/20
    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
    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
    5 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. Google Cloud

    Wonderful customer story

    First-party
    https://cloud.google.com/customers/wonderful
    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 Feb 11, 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 Feb 9, 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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