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Google Cloud Healthcare United Kingdom

Giles AI

Application Refactoring · Document Processing · Real-time Analytics

Bringing a new drug to market typically takes between 10 and 12 years, and around a third of that time is spent on research. With databases like PubMed hosting over 40 million articles, scientists are forced to trawl through reams of literature to find the specific insights needed to launch a clinical trial or regulatory submission.

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85% reduction in time required for clinical research tasks
95% accuracy in medical research data extraction
Delivering 95% data accuracy with

Reported impact

Business outcome

95% accuracy in medical research data extraction; 85% reduction in time required for clinical research tasks; Full healthcare compliance out of the box with Google Cloud; Giles AI uses the integrated, secure Google Cloud AI ecosystem for high-accuracy research insights, accelerating scientific discovery..

  • Compliance
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

giles® also benefits from the flexibility of Google Cloud, which makes it easy to develop solutions for different use cases. Using Model Garden on Vertex AI , the Giles AI team can plug and play different AI models for different tasks. For example, they can use Gemini for complex reasoning or deploy open-source models, such as Gemma , for enterprise clients who require their data to remain within specific geographic borders. Giles AI migrated to Google Cloud with the support of Google Cloud partner Insight, who managed the infrastructure landing platform and helped streamline the transition from its previous cloud provider. Since migrating, giles® has significantly increased its dependability as a robust engine for accelerating scientific decision-making. At the heart of this transformation are Vertex AI and Gemini , which allow the platform to ingest, parse, and analyze millions of complex documents with the accuracy the healthcare industry demands.

Data flow

Source data is captured through Document AI, processed with Gemini Pro, Vertex AI, orchestrated via Google Kubernetes Engine (GKE), and surfaced through Gemini, Cloud Run, Model Garden

Solution architecture

7 components · 4 layers
  1. Ingest
    • Document AI Extracts structured data from documents and forms for downstream workflows.
  2. Compute
    • Gemini Pro Performs higher-depth reasoning and content generation for the workflow.
    • Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
  3. Serving
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
    • Cloud Run Runs application services and proprietary code in a managed container environment.
    • Model Garden Provides access to multiple governed foundation models for experimentation and deployment.
  4. Orchestration
    • Google Kubernetes Engine (GKE) Orchestrates the platform's containerized services and high-concurrency workloads.

Architecture clues

  • # Giles AI delivers 95% accuracy in data extraction with Vertex AI and Gemini
  • 95% accuracy in medical research data extraction
  • Full healthcare compliance out of the box with Google Cloud
  • Giles AI uses the integrated, secure Google Cloud AI ecosystem for high-accuracy research insights, accelerating scientific discovery.
  • Vertex AI and Gemini

Evidence from the source

85% reduction in time required for clinical research tasks
95% accuracy in medical research data extraction
Full healthcare compliance out of the box with Google Cloud
Giles AI uses the integrated, secure Google Cloud AI ecosystem for high-accuracy research insights, accelerating scientific discovery.
The need for secure, reliable AI the
Transparency Evidence & methodology 90/100 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 value90/100
  • 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
    3 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Giles AI customer story

    First-party
    https://cloud.google.com/customers/giles
    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 28, 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 26, 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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