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Google Cloud Manufacturing Indonesia

GEMS

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GEMS uses Google Gemini for GEMVIS, a multi-agent intelligence system that unifies data across 50 applications and drives real-time mining intelligence.

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By providing a real-time "helicopter view" of the entire operation chain, GEMS has effectively lowered the barrier to data accessibility for its 4,000+ users.
Empowered 4,000+ users with a data-driven culture through Google Workspace integration
Scaling specialized AI agents with Gemini to accelerate executive decision-making by 90%
The impact of GEMVIS has been transformative, accelerating decision-making speed by over 90% and condensing complex insight generation from hours into seconds or minutes.

Reported impact

Business outcome

is a Google Cloud partner in Indonesia that provides fast knowledge of cutting-edge cloud and AI technologies.

  • Productivity Improvement
  • Speed to Market

Solution

GEMS uses Google Gemini for GEMVIS, a multi-agent intelligence system that unifies data across 50 applications and drives real-time mining intelligence. To reduce the Total Cost of Ownership (TCO) and accelerate delivery, GEMS onboarded Google Cloud as its primary infrastructure provider. Utilizing Google Compute Engine , the DigiTech division built a secure foundation to manage its vast application ecosystem. To protect these digital assets, the organization implemented for infrastructure security and used Cloud Monitoring for complete observability. This selection was further aligned with company-wide standards for , ensuring that the new intelligence system could seamlessly serve its large user base while maintaining a secure-by-design architecture.

Solution architecture

6 components · 4 layers
  1. Compute
    • Compute Engine Runs containerized or virtualized application workloads with the elasticity needed for production scale.
    • Google Kubernetes Engine (GKE) Runs containerized or virtualized application workloads with the elasticity needed for production scale.
  2. Serving
    • Google Maps Platform Adds geospatial context used to score locations, assets, or routes in the solution.
    • Google Workspace Provides the collaboration surface where teams review outputs and work with AI assistance.
  3. Orchestration
    • Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
  4. Governance
    • Google Cloud Armor Adds governance, security, and control to the production AI and data environment.

Architecture clues

  • GEMS uses Google Gemini for GEMVIS, a multi-agent intelligence system that unifies data across 50 applications and drives real-time mining intelligence.
  • To address its data challenges, GEMS developed GEMVIS (GEMS Valuable Intelligence System), a hierarchical multi-agent system led by Eky Pratama Halim and Tijani Rijal Fadhilah, Chief Engineers at GEMS.

Evidence from the source

Accelerated executive decision-making speed by over 90% using Google Gemini
GEMS accelerates pit-to-port mining intelligence and decisions by 90% with Gemini
Optimized infrastructure performance and security through Google Kubernetes Engine (GKE) and Google Cloud Armor
Reduced multi-operational data retrieval time from two days to under one hour through Gemini platform
To reduce the Total Cost of Ownership (TCO) and accelerate delivery, GEMS onboarded Google Cloud as its primary infrastructure provider. Utilizing Google Compute Engine , the DigiTech division built a secure foundation t
Transparency Evidence & methodology 81/100 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 value81/100
  • 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 impact25/25
    2 metrics 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
    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 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

    GEMS customer story

    First-party
    https://cloud.google.com/customers/gems
    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 Jun 8, 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 Oct 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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