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AWS Software / SaaS Global

Attensi

Document Processing

Attensi needed a technical setup where data is both protected and isolated while being accessible to teams to work with. Finally, the company found that AWS had the required flexibility, quality, and reliability.

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2.5x
90–95%
Moreover, using Creator with AI copilots, companies can accelerate content creation by 90–95 percent.
faster content creation 90–95%

Reported impact

Business outcome

Attensi’s innovative, AI-enhanced solution empowers customers to accelerate and expand their content-generation capabilities, and Attensi is ready to do more. Working alongside the AWS team, Attensi will continue its journey of growth, further enhancing Creator and implementing more core AI offerings and features. Key reported metrics include faster content creation 90–95%; 90–95%; productivity increase 19%.

  • Productivity Improvement
  • Revenue Growth

Solution

Attensi Uses the Power of AI to Reduce the Time It Takes to Develop Content by up to 95% Since 2013, Attensi provides gamified training solutions—including simulations in 3D virtual environments; virtual-reality solutions; and its flagship solution, Creator, which companies use to create training content in-house. Attensi has embraced generative AI in its software products and Creator. Attensi is enhancing its simulation training software, hosted on Amazon Web Services (AWS), with generative artificial intelligence (AI). Using a suite of AWS services, the company is increasing scalability, improving security, and adding more AI-powered features to its training products and platform. This helps Attensi improve the client experience—including AI-powered dialogues, fast automatic translations, faster content generation, and generative AI copilot services. Source data enters the Attensi workflow through its application services, processed with Amazon EKS, Amazon SageMaker, Amazon Bedrock.

Data flow

Source data enters the Attensi workflow through its application services, processed with Amazon EKS, Amazon SageMaker, Amazon Bedrock.

Solution architecture

4 components · 1 layer
  1. Compute
    • Amazon EKS Runs containerized workloads and Kubernetes-based application services.
    • Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • Hugging Face Provides access to pretrained models that the customer adapts for domain use cases.

Architecture clues

  • Named components in the deployment include Amazon EKS, Amazon SageMaker, Amazon Bedrock, Hugging Face.

Evidence from the source

2.5x
90–95%
Attensi needed a technical setup where data is both protected and isolated while being accessible to teams to work with. Finally, the company found that AWS had the required flexibility, quality, and reliability.
Attensi’s first-party AI solutions are crafted using multiple services from AWS and Hugging Face , an AWS Partner .
Moreover, using Creator with AI copilots, companies can accelerate content creation by 90–95 percent.
These services include Amazon Bedrock , a fully managed service that offers a choice of high-performing foundation models, and Amazon SageMaker , which brings together a broad set of tools to empower high-performance, low-cost machine learn
Transparency Evidence & methodology 95/100 reference value · 87% 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 value95/100
  • 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
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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
    6 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 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. AWS

    Attensi customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/attensi/
    Source type
    Vendor-published customer story
    Published
    May 20, 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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