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.

Products

Case Studies

Quicklinks

Contact

omni:us is proud to be one of Europe‘s Horizon2020 SME-Instrument and Fet Flag Champions.
We are grateful to have received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 820323; 850053.

Co-financed by European Fund for Regional Development (EFRE)
Pro Fit-Project “Vollautomatisierung der Wertschöpfungskette im Digitalisierungsprozess von Archivdaten” with support of IBB/EFRE in 2016/2017.

Copyright © 2025 Qidenus Group GmbH. All Rights Reserved.

Privacy | Imprint