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Case Study

EU Top 10 Insurer
deploys custom AI models
in weeks ​

An EU Top 10 expanded automation into niche claims lines without adding data science headcount by leveraging omni:us’ pre-trained, insurance-native and self-service AI Modeler Platform.

Our Measured Impact

Cost Savings

0

Need for Additional Data Science Hires

Integration Speed

0

Days for first peril go-live

The Insurer's Challenge

  • Niche claims lines required specialized AI models not available off-the-shelf
  • In-house data science resources were limited and focused on core automation projects
  • Manual processing of these claim types was slow, costly, and inconsistent
  • Insurer needed a fast, cost-effective way to train, deploy, and maintain custom AI without disrupting existing systems

Our Implemented Solution

  • Used omni:us AI Modeller to train custom models for low-frequency, high-complexity claim types
  • Leveraged omni:us’ pre-trained base models to reduce training data requirements and shorten timelines
  • Applied hyperparameter tuning and targeted data selection for maximum accuracy
  • Integrated custom models into live workflows alongside standard omni:us automation

Why This Insurer Chose omni:us

  • Insurance-native base models trained on millions of P&C claims
  • Self-service UI for model training — no coding required
  • Ability to verticalize performance for highly specific perils
  • Direct integration with existing omni:us and core claims workflows

Key Takeaways

  • Custom AI is achievable without heavy infrastructure or new teams
  • Leveraging pre-trained, insurance-native models accelerates development dramatically
  • Self-service AI tools empower insurers to expand automation in any direction

Build the Agentic Specialists Your Business Needs

We’ll help you scope, train, and deploy your first custom model in under two months — and integrate it directly into your claims workflows.

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omni:us ist stolzer Europas Horizon2020 SME-Instrument and Fet Flag Champion.
Wir sind dankbar für die Förderung durch das Forschungs- und Innovationsprogramm Horizon 2020 der Europäischen Union unter der Finanzhilfevereinbarung Nr. 820323; 850053.

Kofinanziert von Europäischen Fonds für regionale Entwicklung (EFRE)
Pro Fit-Projekt „Vollautomatisierung der Wertschöpfungskette im Digitalisierungsprozess von Archivdaten“ mit Unterstützung von IBB/EFRE in 2016/2017.

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