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Oracle Healthcare Global

Cerebriu

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Cerebriu needed scalable and affordable GPU infrastructure so it could train data-intensive radiology AI models faster and support the company's global expansion plans without overspending on cloud compute.

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3X faster results than AWS and Azure for the same price
Serves hospitals across Europe, the Middle East, and Asia
Training time dropped from eight weeks to three

Reported impact

Business outcome

Cerebriu accelerated radiology model training, reduced the time needed to build new models, and created a scalable GPU foundation for hospital and imaging-partner growth across multiple regions.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Cerebriu moved AI model training for its radiology software onto Oracle Cloud Infrastructure using bare metal compute instances with NVIDIA GPUs. The OCI environment gives the startup higher GPU availability, more GPU memory for the same price, and easier instance launch than its alternatives, allowing the team to run parallel image-processing jobs that improve model-training speed and accuracy. Those models are used in brain MRI workflows to detect possible critical findings during scanning and help ensure the right images are captured early enough for radiologists to intervene sooner. By centralizing training on OCI AI infrastructure, Cerebriu shortened model development cycles, preserved headroom for future inferencing workloads, and built a cloud platform that can scale with MRI equipment partners and hospital deployments across Europe, the Middle East, and Asia.

Data flow

MRI image data is processed and used to train models on OCI GPU infrastructure, where high-memory instances accelerate parallel image processing; the resulting models support Cerebriu's software for detecting critical findings during scans and are planned to extend into OCI-based inferencing for hospital workflows.

Solution architecture

2 components · 1 layer
  1. Compute
    • Oracle Cloud Infrastructure (OCI) Compute bare metal instances with NVIDIA GPUs Provides the high-memory GPU training environment for Cerebriu's radiology AI models.
    • OCI AI infrastructure Hosts the scaled training environment that Cerebriu plans to extend to inferencing for MRI workflows.

Architecture clues

  • Cerebriu plans to add inferencing on OCI after centralizing model training there
  • High-memory GPU instances support parallel image processing during training
  • OCI distributed cloud regions are planned for broader hospital coverage

Evidence from the source

After moving all of its AI model training to OCI AI infrastructure with NVIDIA A100 40GB GPUs, Cerebriu accelerated parallel processing by 3X.
Also, training time dropped from eight weeks to three for greater efficiency.
For the same price, we get more GPU compute and memory for results that are 3X faster than AWS and Azure.
Transparency Evidence & methodology 90/100 reference value · 74% 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 depth25/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence74%
  • 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 evidence15/20
    3 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. Oracle

    Cerebriu customer story

    First-party
    https://www.oracle.com/customers/cerebriu/
    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 Jul 21, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    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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