Insurance
AI Modeller

The Insurance AI Modeller gives you the power to train, deploy, and refine custom AI models for your unique claims portfolio — all on our proven omni:us platform that runs any model safely and at enterprise scale.

For Insurers Who

Want to expand automation beyond standard property, motor, and liability
Need self-service model training without hiring a data science team
Have niche lines of business or rare perils that require custom handling
Require rapid deployment of new AI capabilities in response to market changes

Core Outcomes

Faster
Time-to-Value

Custom models live in 4–8 weeks

with continuous performance measurement and retraining via automated feedback loops

Lower Operational Risk

Build on proven omni:us base models

Design your custom models. Quick and easy in self-service.

Scalable Innovation

Continuously adapt models to new perils

Run any custom model at scale. Start small and add more data over time.

Why omni:us AI Modeler Is Different

Insurance-native starting point: Not a generic AI - purpose-built on P&C claims and decisions
Self-service & intuitive: Bring your own data, UI-based, no coding required
Agentic-ready: Custom models integrate directly into omni:us workflows and AI agents
Full transparency: Model results are explainable, bias-tested & compliant with the AI Act regulations

Build the Agentic Specialists Your Business Needs

Create custom AI agents that handle your most specific claims challenges.

We’ll show you how to scope, train, and deploy your first specialist model in under two months.

Related Case Study

AI Modeler

EU Top 10 Insurer Deploys Custom AI Models in Weeks

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LEADING GLOBAL INNOVATOR

Global Innovation Champion

Prominent claims provider

LEADING GLOBAL INNOVATOR

Global Innovation Champion

Prominent claims provider

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.