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AWS Software / SaaS global

Miro

Enterprise Knowledge Search

Miro helps distributed teams of all sizes collaborate and use the solution to lead engaging workshops and meetings, design products, brainstorm ideas, and more. “Miro is redefining how teams that bring new products and services to market can get projects from idea to launch, helping them unlock the huge potential of generative AI,” says Ayala Ohyon, head of data science at Miro.

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Reported impact

Business outcome

“By adopting a standardized approach built on AWS, we are empowered to rapidly create cutting-edge AI models and help Miro users benefit from the latest AI developments.” By adopting a standardized approach built on AWS, we are empowered to rapidly create cutting-edge AI models and help Miro users benefit from the latest AI developments.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Always striving to be on the cutting edge, Miro began to explore the potential of generative artificial intelligence (AI) to support its users. Miro Assist consists of a diverse set of AI features built on Amazon Web Services (AWS). Using services like Amazon Elastic Kubernetes Service (Amazon EKS), a managed service for running Kubernetes on AWS, Miro can quickly deploy and scale its AI features, relying on expert support and flexible security provisioning from AWS. “Miro is redefining how teams that bring new products and services to market can get projects from idea to launch, helping them unlock the huge potential of generative AI,” says Ayala Ohyon, head of data science at Miro.

Data flow

“It’s very helpful to have direct contact with people at AWS who are fluent in the resources and can give advice and assurance that we’re maximizing the full potential of the current technology.” Using AWS, data is encrypted at rest and in transit, and access to data is controlled through AWS Identity and Access Management (AWS IAM), which securely manages identities and access to AWS services and resources.

Solution architecture

2 components · 2 layers
  1. Compute
    • Amazon EKS Runs and scales Kubernetes-based application and analytics services.
  2. Governance
    • AWS IAM Controls identities, permissions, and secure access to cloud resources.

Architecture clues

  • Using Amazon EKS, Miro can scale as needed to reduce overprovisioning while maintaining low latency, low error rates, and a stable solution.

Evidence from the source

Because of its successful history on AWS, Miro pursued using Amazon EKS for a consistent deployment process for generative AI models.
Miro developed an architecture to serve as a blueprint for setting up new AI microservices.
Miro helps distributed teams of all sizes collaborate and use the solution to lead engaging workshops and meetings, design products, brainstorm ideas, and more.
“Miro is redefining how teams that bring new products and services to market can get projects from idea to launch, helping them unlock the huge potential of generative AI,” says Ayala Ohyon, head of data science at Miro.
“The collaboration we have with AWS representatives gives us confidence that we’re using the technology in the optimal way,” says Ohyon.
Transparency Evidence & methodology 61/100 reference value · 85% 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 value61/100
  • 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 specificity16/20
    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 confidence85%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    Miro customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/miro-generative-ai/
    Source type
    Vendor-published customer story
    Published
    Mar 1, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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