Google Cloud Software / SaaS Singapore

Whale

IT Operations Automation

Large omnichannel businesses have to manage an enormous amount of information. Combining data from physical spaces, like camera footage for security, with data from digital channels, like customer interactions, creates a portfolio of deeply valuable insights.

The company’s unified AI architecture also helps Whale optimize AI response speeds and reduce latency for its customers. This increased speed has led to higher user engagement and higher completion rates across its product workflows. For end users, this all translates to smoother natural-language interactions, enabling store operations and sales teams to automate 24/7 compliance audits and access real-time coaching at a moment's notice.

Reported impact

Business outcome

Integrated multimodal Gemini models into Whale solutions; Accelerated model deployment and tuning with Gemini Enterprise Agent Platform; Automate data ingestion to inform operational decisions; Offer real-time coaching to sales teams.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

Whale integrates Gemini multimodal AI to help enterprises evolve from informational black boxes to intelligent workspaces. Large omnichannel businesses have to manage an enormous amount of information. Combining data from physical spaces, like camera footage for security, with data from digital channels, like customer interactions, creates a portfolio of deeply valuable insights. However, collecting and collating that information is highly manual, and the data is often fragmented across many tools. Whale is an AI platform built to run end-to-end enterprise operations. Through its connected solutions —SpaceSight, Echo, Harbor, Lume, and Alivia—Whale provides a unified solution that enables global brands to see, hear, remember, express, and think through complex operational challenges.

Solution architecture

2 components · 2 layers
  1. Compute
    • Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
  2. Orchestration
    • Gemini Enterprise Agent Platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • The company’s unified AI architecture also helps Whale optimize AI response speeds and reduce latency for its customers. This increased speed has led to higher user engagement and higher completion rates across its product workflows. For en
  • Whale integrated multimodal Gemini models into Alivia to build a strong foundation for improved long-context performance. With the Gemini Enterprise Agent Platform (formerly known as Vertex AI), the team accelerated model deployment, tuning

Evidence from the source

Accelerated model deployment and tuning with Gemini Enterprise Agent Platform
Automate data ingestion to inform operational decisions
Integrated multimodal Gemini models into Whale solutions
Offer real-time coaching to sales teams
The company’s unified AI architecture also helps Whale optimize AI response speeds and reduce latency for its customers. This increased speed has led to higher user engagement and higher completion rates across its product workflows. For en
Whale integrated multimodal Gemini models into Alivia to build a strong foundation for improved long-context performance. With the Gemini Enterprise Agent Platform (formerly known as Vertex AI), the team accelerated model deployment, tuning
Transparency Evidence & methodology 71/100 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 value71/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    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

    Whale customer story

    First-party
    https://cloud.google.com/customers/whalesg
    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

  • Sitemap last modified Sep 7, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Sep 7, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.