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AWS Software / SaaS Switzerland

Enterprise Bot

Customer Service Automation

With the opportunity to expand into new markets but with a need for better performance and flexibility, Enterprise Bot , an enterprise generative artificial intelligence (AI) company, migrated from a previous cloud provider to Amazon Web Services (AWS).

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Achieving 10.5 Percent Higher AI Accuracy on AWS than on Competing Providers Enterprise Bot uses cutting-edge generative AI to provide its clients with solutions that handle 60–80...
Using Anthropic's Claude 3 in Amazon Bedrock , Enterprise Bot built an AI solution that achieved 84 percent accuracy, 10.5 percent higher than that of competing AI providers.

Reported impact

Business outcome

With the opportunity to expand into new markets but with a need for better performance and flexibility, Enterprise Bot , an enterprise generative artificial intelligence (AI) company, migrated from a previous cloud provider to Amazon Web Services (AWS).

  • Customer Experience
  • Scalability

Solution

With the opportunity to expand into new markets but with a need for better performance and flexibility, Enterprise Bot , an enterprise generative artificial intelligence (AI) company, migrated from a previous cloud provider to Amazon Web Services (AWS). Using AWS, the company enhances customer and employee support with AI offerings that deliver more accurate and concise responses for users and customer service agents. Migrating to AWS for Better Flexibility and Speed Founded in 2017 and based in Switzerland, Enterprise Bot brings cutting-edge generative AI capabilities to enterprises by offering sophisticated AI bots with the option to choose from multiple large language models (LLMs). The company built its platform in the cloud, but with the LLM and generative AI landscape evolving rapidly, its previous cloud provider couldn't meet the growing demands for performance and flexibility.

Data flow

Enterprise Bot received technology support and market insights throughout the process. Now, the company can focus on the customer experience and automating key processes.

Solution architecture

1 components · 1 layer
  1. Compute
    • Amazon Bedrock Runs the foundation model layer for the customer and agent support workflows.

Architecture clues

  • Achieving 10.5 Percent Higher AI Accuracy on AWS than on Competing Providers Enterprise Bot uses cutting-edge generative AI to provide its clients with solutions that handle 60–80 percent of all...
  • Migrating to AWS for Better Flexibility and Speed Founded in 2017 and based in Switzerland, Enterprise Bot brings cutting-edge generative AI capabilities to enterprises by offering sophisticated AI...

Evidence from the source

"From the start, the collaboration with the AWS team was open and the support loop quick.
Enterprise Bot migrated its AI workloads to AWS and adopted Amazon Bedrock, a fully managed service for building generative AI applications with security, privacy, and responsible AI.
Migrating to AWS for Better Flexibility and Speed Founded in 2017 and based in Switzerland, Enterprise Bot brings cutting-edge generative AI capabilities to enterprises by offering sophisticated AI bots with the option to choose from...
To address these needs, the company evaluated new providers and ultimately chose AWS to support its technical and business goals.
Using AWS, the company enhances customer and employee support with AI offerings that deliver more accurate and concise responses for users and customer service agents.
With the opportunity to expand into new markets but with a need for better performance and flexibility, Enterprise Bot , an enterprise generative artificial intelligence (AI) company, migrated from a previous cloud provider to Amazon Web...
Transparency Evidence & methodology 86/100 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 value86/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Enterprise Bot customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/enterprise-bot/
    Source type
    Vendor-published customer story
    Published
    Jul 31, 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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