Case Studies

Selected automation & AI projects

The following case studies showcase how automation and artificial intelligence are applied across different industries – each with a clear process focus, measurable impact, and full integration into daily operations.

Generative AI for internal audit reporting
Healthcare

Generative AI for internal audit reporting

Automated creation of audit reports based on internal guidelines and past audits.

  • 85% reduction in report creation effort
  • Higher consistency and comparability across audits
  • Human-in-the-loop review for final approval
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AI research tool for deal screening & due diligence
Finance / Private Equity

AI research tool for deal screening & due diligence

Agent-based research across up to 200 internal and external sources per target.

  • Structured, investment-ready research reports
  • Higher dealflow and faster target identification
  • Enabled investments beyond core industries
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AI-powered claims assessment from photos
Insurance

AI-powered claims assessment from photos

Automated damage estimation and workflow support in the claims process.

  • Approx. one third of claims processed with AI support
  • Shorter processing times and lower handling costs
  • More consistent damage assessments
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AI-powered contract review & drafting
Legal

AI-powered contract review & drafting

Automated clause analysis, risk identification, and contract draft creation.

  • Faster contract review and drafting
  • Higher consistency and transparency
  • Legal decision authority remains fully with humans
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AI-powered internal knowledge platform across teams
Software

AI-powered internal knowledge platform across teams

Central knowledge base for development, support, and management with controlled customer access.

  • Unified access to internal knowledge
  • Significantly reduced search and coordination effort
  • Controlled self-service for selected customer content
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AI-powered production knowledge & forecasting
Manufacturing

AI-powered production knowledge & forecasting

Centralization of production knowledge to support planning and forecasting.

  • Improved forecast accuracy
  • Lower error rates in planning and production
  • Reduced dependency on individual experience
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