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Microsoft Healthcare Switzerland

amaise

Document Processing · IT Operations Automation · Security and Compliance

amaise was founded in Switzerland in 2019. Its core mission? To tackle one central challenge in the healthcare and insurance sector: the need to process unstructured medical case histories. The data comes from all kinds of sources-including doctor's reports, laboratory findings, expert assessments

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Collaboration with Baloise, one of Switzerland's biggest insurers, illustrates how amaise is revolutionizing complex processes. The insurance company teamed up with amaise back in 2020 to launch a pilot project in its Pe...
The latest feedback from customers shows that, with amaise, insurance companies can process cases up to 80% faster, with as much as four times greater accuracy and payout outcomes optimized by as much as 11%.
amaise lets insurance companies process cases up to 80% faster, with up to four times greater accuracy and with payout outcomes optimized by as much as 11% - delivering faster and better results.
amaise signed up for the Microsoft for Startups Founders Hub program in 2023.

Reported impact

Business outcome

amaise lets insurance companies process cases up to 80% faster, with up to four times greater accuracy and with payout outcomes optimized by as much as 11% - delivering faster and better results.

  • Compliance
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

amaise was founded in Switzerland in 2019. Its core mission? To tackle one central challenge in the healthcare and insurance sector: the need to process unstructured medical case histories. The data comes from all kinds of sources-including doctor's reports, laboratory findings, expert assessments amaise was looking for a solution that would make the complex processing of unstructured data sets in the healthcare and insurance sector significantly faster and easier. With the aid of Microsoft Azure, amaise is revolutionizing medical insurance claims management. AI models analyze unstructured data and help expensive insurance specialists to make better decisions more quickly - speeding up healthcare and insurance processes and improving the accuracy of outcomes. amaise is a Swiss insurtech startup whose ultra-modern GenAI solution helps insurance companies process and assess medical insurance claims more efficiently.

Data flow

Source content is gathered from the organization's documents and knowledge repositories, Azure OpenAI Service interprets or generates responses from that content, and the resulting answers or assessments are surfaced to employees or citizens through the application workflow.

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure OpenAI Service Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.

Architecture clues

  • Azure OpenAI Service: Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.
  • With the aid of Microsoft Azure, amaise is revolutionizing medical insurance claims management. AI models analyze unstructured data and help expensive insurance specialists to make...
  • amaise is a Swiss insurtech startup whose ultra-modern GenAI solution helps insurance companies process and assess medical insurance claims more efficiently.
  • amaise lets insurance companies process cases up to 80% faster, with up to four times greater accuracy and with payout outcomes optimized by as much as 11% - delivering faster and...
  • amaise was founded in Switzerland in 2019. Its core mission? To tackle one central challenge in the healthcare and insurance sector: the need to process unstructured medical case h...
  • amaise was looking for a solution that would make the complex processing of unstructured data sets in the healthcare and insurance sector significantly faster and easier.

Evidence from the source

With the aid of Microsoft Azure, amaise is revolutionizing medical insurance claims management. AI models analyze unstructured data and help expensive insurance specialists to make better decisions more quickly - speedin...
amaise lets insurance companies process cases up to 80% faster, with up to four times greater accuracy and with payout outcomes optimized by as much as 11% - delivering faster and better results.
amaise signed up for the Microsoft for Startups Founders Hub program in 2023.
amaise was founded in Switzerland in 2019. Its core mission? To tackle one central challenge in the healthcare and insurance sector: the need to process unstructured medical case histories.
amaise was looking for a solution that would make the complex processing of unstructured data sets in the healthcare and insurance sector significantly faster and easier.
Transparency Evidence & methodology 95/100 reference value · 92% confidence · 2 sources

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 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references 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. Microsoft

    amaise customer story

    First-party
    https://www.microsoft.com/en/customers/story/21393-legal-i-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Jan 28, 2025
    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
  2. amaise

    Official Information About amaise

    First-party
    https://www.amaise.com/official-information-about-amaise

    The official information page says "Headquarters: Bern, Switzerland."

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Jan 28, 2025 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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