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Google Cloud Commercial and Professional Services Switzerland

VELLOWS

Enterprise Knowledge Search · Sales Automation

VELLOWS needed a secure AI platform that could standardize knowledge management and accelerate proposal writing, but strict privacy rules and disconnected expertise repositories kept consultants from safely using AI at scale.

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Achieves 14x faster proposal creation
Standardizes document search and email drafting for 40+ consultants
VELLOWS uses Gemini Enterprise to secure data, standardize knowledge management, and accelerate complex proposal writing by 14x.

Reported impact

Business outcome

VELLOWS dramatically accelerated proposal creation, standardized internal communication quality, and created a governed base for future AI-driven client offerings.

  • Compliance
  • Productivity Improvement
  • Speed to Market

Solution

VELLOWS uses Google Cloud to address this challenge: VELLOWS needed a secure AI platform that could standardize knowledge management and accelerate proposal writing, but strict privacy rules and disconnected expertise repositories kept consultants from safely using AI at scale. Gemini Enterprise provides the secure enterprise AI layer for proposal drafting, document analysis, and future digital services, serving as the serving layer. Gemini Notebook analyzes complex project documents and extracts technical answers during live work, serving as the serving layer. Agent Designer lets non-technical users build no-code personal agents for routine work such as drafting emails, serving as the serving layer. Agent Search enables natural-language retrieval across company files in SharePoint and Outlook, serving as the serving layer. Cloud Storage stores finalized project documents that feed the knowledge engine surfaced in Gemini Enterprise, serving as the storage layer.

Data flow

Final project documents and metadata move from the ERP system into Cloud Storage, are indexed into a structured store, and are then surfaced through Gemini Enterprise, Agent Search, and Gemini Notebook so consultants can retrieve answers and draft proposals from a governed knowledge base.

Solution architecture

6 components · 2 layers
  1. Storage
    • Cloud Storage stores finalized project documents that feed the knowledge engine surfaced in Gemini Enterprise
  2. Serving
    • Gemini Enterprise provides the secure enterprise AI layer for proposal drafting, document analysis, and future digital services
    • Gemini Notebook analyzes complex project documents and extracts technical answers during live work
    • Agent Designer lets non-technical users build no-code personal agents for routine work such as drafting emails
    • Agent Search enables natural-language retrieval across company files in SharePoint and Outlook
    • Cloud Run is planned to host additional ERP connectors that operationalize more structured company data

Architecture clues

  • Future Cloud Run connectors will use structured staffing and capacity data for consultant matching
  • The company expanded beyond its initial 39 Gemini Enterprise licenses as adoption grew
  • The deployment uses identity federation and role configuration to satisfy Swiss and EU residency expectations

Evidence from the source

A custom data pipeline moves these files to Cloud Storage on Google Cloud.
Achieves 14x faster proposal creation
Standardizes document search and email drafting for 40+ consultants
VELLOWS enables employees to use natural language to instantly locate specific files across SharePoint and Outlook.
VELLOWS uses Gemini Enterprise to secure data, standardize knowledge management, and accelerate complex proposal writing by 14x.
Transparency Evidence & methodology 96/100 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 value96/100
  • 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 specificity16/20
    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

    VELLOWS customer story

    First-party
    https://cloud.google.com/customers/vellows
    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 Sep 17, 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 Sep 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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