Use Case Intelligence RSS Search use cases Search
Microsoft Software / SaaS Switzerland

Agnostic Intelligence

Risk Management · Security and Compliance · Supply Chain Optimization

Agnostic Intelligence, a Swiss start-up, developed an AI-based platform to tackle supply chain risks. Using advanced algorithms and machine learning, it provides real-time insights and proactive risk management, addressing cyber-attacks, compliance, and legal risks.

Read the original story Explore all use cases
Deploying Microsoft solutions, especially Azure OpenAI Service, transformed Agnostic Intelligence's operations by eliminating time-consuming tasks, saving users up to 80% of their time, and enabling IT managers to focus
The platform's AI analytics capabilities are now so advanced that users can save up to 80% of their time.
This means that the sometimes extensive and time-consuming documentation, risk assessment, and control requirements can be met quickly and easily.
We were able to rely on our contacts at all times and in all matters.

Reported impact

Business outcome

With Microsoft's support, Agnostic Intelligence will continue to develop and improve its analytical tools. The challenges facing companies in a globalized world are becoming increasingly complex: cyberattacks can cause significant economic and reputational damage. Global supply chains mean that labour standards or environmental requirements must be complied with in very different regions of the world.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Agnostic Intelligence built this solution on Azure OpenAI Service, Azure AI Services, Azure App Service, Azure SQL Database. Agnostic Intelligence, a Swiss start-up, developed an AI-based platform to tackle supply chain risks. Azure OpenAI Service provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow. Azure AI Services applies managed AI models to analyze content and automate business decisions. Azure App Service hosts the web application and API endpoints for the solution. Azure SQL Database stores transactional and reporting data that the application depends on.

Data flow

Operational data is collected from line-of-business systems, processed inside the analytics platform before Power BI dashboards surface insights to business users.

Solution architecture

6 components · 3 layers
  1. Compute
    • Azure OpenAI Service Provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow.
    • Azure AI Services Applies managed AI models to analyze content and automate business decisions.
    • Azure App Service Hosts the web application and API endpoints for the solution.
    • Azure Provides the core cloud infrastructure, security boundary, and managed services underpinning the solution.
  2. Storage
    • Azure SQL Database Stores transactional and reporting data that the application depends on.
  3. Serving
    • Power BI Publishes dashboards and self-service analytics to business users.

Architecture clues

  • Containerized workloads are part of the runtime architecture.

Evidence from the source

Agnostic Intelligence used Microsoft solutions like Azure Cognitive Services, Azure App Service, Azure Containers, Azure Developer Tools, Azure SQL Database, Power BI, and Azure OpenAI Service to develop their AI-based p
Agnostic Intelligence, a Swiss start-up, developed an AI-based platform to tackle supply chain risks.
Deploying Microsoft solutions, especially Azure OpenAI Service, transformed Agnostic Intelligence's operations by eliminating time-consuming tasks, saving users up to 80% of their time, and enabling IT managers to focus
We were able to rely on our contacts at all times and in all matters.
With Microsoft's support, Agnostic Intelligence will continue to develop and improve its analytical tools.
Transparency Evidence & methodology 95/100 reference value · 89% 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
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Microsoft

    Agnostic Intelligence customer story

    First-party
    https://www.microsoft.com/en/customers/story/18699-agnostic-intelligence-azure-marketplace
    Source type
    Vendor-published customer story
    Published
    Sep 16, 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

  • Published on source page Sep 16, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
OpenAI Oct 14, 2026 SentinelOne Software / SaaS North America SentinelOne needed to reverse engineer sophisticated malware far faster than specialist researchers could do manually so it could investigate more samples, spot relationships across threats, and extend scarce cyber… 8 hours to reverse-engineer malware, down from 4–6 weeks Risk ManagementSecurity and Compliance OpenAI Daybreak · OpenAI models Google Cloud Archived · Sep 17, 2026 Parsewise Software / SaaS United Kingdom For risk specialists in the financial services and insurance industries, reading can feel like a full-time job. Whether assessing an insurance claim or approving a loan, these subject matter experts must search through… 29% lower error rate than best-performing LLM on grounded-reasoning benchmark Document ProcessingRisk Management Gemini · Gemini 3.0 Flash · Gemini 3.5 Flash Databricks Apr 23, 2026 Elliptic Software / SaaS Global Elliptic's clients—compliance teams at crypto exchanges, banks, payment providers, and institutional custody platforms—face rising alert volumes and stringent regulation, and analysts must document each decision by… 2x faster resolution of high-risk screenings Fraud DetectionRisk Management Agent Bricks / Mosaic AI · Databricks Foundation Model APIs · Databricks Model Serving