We built this because the adjuster shortage is not going away

Why travel insurance

The right starting point for claims automation

Travel insurance claims are structurally different from most other insurance lines. The triggering events are observable facts: a flight was delayed by a documented number of hours, a destination was placed under a government travel advisory, a medical event occurred in a specific country. The policy terms that govern these events are standardised and repeat across large volumes.

This combination makes travel claims the natural first target for automation. The data is clean, the policies are readable, and the coverage triggers are verifiable without investigator judgment. A machine can handle the structured majority; your adjusters can focus on the edge cases that genuinely need them.

Saverio spent eight years building data systems for claims operations teams at regional insurers. He watched adjusters manually copy claim data between systems, open policy PDFs to find coverage clauses they had already looked up a hundred times, and write structured decisions in free-text fields that no system could read back. The inefficiency was not a people problem. It was an architecture problem.

Livia came from NLP and document-understanding work, building extraction pipelines for financial-services documents. When Saverio described what a travel insurance policy looked like and what a claims adjuster needed to extract from it in thirty seconds, she recognised it immediately as a solvable document-understanding problem, not a general AI challenge.

Insurteam was incorporated in Zurich in 2025. The first version of the pipeline processes flight-delay claims end to end: FNOL intake, policy clause extraction, delay threshold verification, parametric payout calculation, and structured settlement decision returned to the insurer's core system. The insurer initiates settlement. Insurteam is the tooling that makes the decision, not the entity that makes the payment.