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Google Cloud Software / SaaS Vietnam

igot.ai

Document Processing · Enterprise Knowledge Search

igot.ai needed a governed AI operating model for audit and enterprise workflows, where teams could search and reason over complex document repositories without maintaining heavy custom infrastructure.

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Reported impact

Business outcome

igot.ai increased audit evidence discovery, reduced data-preparation effort, and cut the specialist staffing needed to operate complex AI workflows.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

igot.ai uses Google Cloud to address this challenge: igot.ai needed a governed AI operating model for audit and enterprise workflows, where teams could search and reason over complex document repositories without maintaining heavy custom infrastructure. BigQuery grounds analysis in enterprise data and gives the platform a scalable foundation for document and workflow context, serving as the storage layer. Gemini Enterprise Agent Platform provides the managed environment where the platform builds and deploys AI capabilities with governance, serving as the serving layer. Gemini Pro reasons over documents and generates structured outputs for specialized workflow agents, serving as the compute layer. Google Kubernetes Engine runs and scales the multi-agent workflows that coordinate retrieval, analysis, validation, and execution, serving as the orchestration layer.

Data flow

Enterprise data is organized in BigQuery, Gemini models on Gemini Enterprise Agent Platform analyze documents and produce structured results, and GKE orchestrates the specialized agents that retrieve, validate, and act on that information in governed workflows.

Solution architecture

4 components · 4 layers
  1. Compute
    • Gemini Pro reasons over documents and generates structured outputs for specialized workflow agents
  2. Storage
    • BigQuery grounds analysis in enterprise data and gives the platform a scalable foundation for document and workflow context
  3. Serving
    • Gemini Enterprise Agent Platform provides the managed environment where the platform builds and deploys AI capabilities with governance
  4. Orchestration
    • Google Kubernetes Engine runs and scales the multi-agent workflows that coordinate retrieval, analysis, validation, and execution

Architecture clues

  • State-audit use cases reduced data preparation effort by 87.5%
  • The platform reached a 95% completion rate for multi-agent workflows on GKE
  • The specialist team required to operate the platform dropped from 12 engineers to four

Evidence from the source

The company also achieved a 95% completion rate for multi-agent workflows running on GKE.
The platform increased evidence discovery by more than 300% while reducing data preparation effort by 87.5%.
The platform uses BigQuery to ground analysis in enterprise data, Gemini models in GEAP to reason over documents and generate structured outputs, and GKE to run and scale the agent workflows behind each use case.
Transparency Evidence & methodology 65/100 reference value · 75% confidence · 2 sources

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 value65/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence75%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency9/10
    2 source references 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

    igot.ai customer story

    First-party
    https://cloud.google.com/customers/igot
    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
  2. TracuuNhanh

    CÔNG TY TNHH IGOT AI

    Independent secondary
    https://tracuunhanh.vn/0318335897-cong-ty-tnhh-igot-ai

    The listing gives the address "Lầu 4, 55 Bis Nguyễn Văn Thủ, Phường Đa Kao, Quận 1, Thành phố Hồ Chí Minh, Việt Nam."

    Source type
    Structured company profile
    Supports
    • Region

Date evidence

  • Sitemap last modified Jun 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jun 9, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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