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Irish-founded insurtech Nettle raises $4.8m seed round

Nettle’s platform uses AI to complete risk assessments by up to five-times faster. 8m seed round

Irish-founded insurtech Nettle raises $4.8m seed round

Irish-founded AI insurance technology start-up Nettle has secured a $4.8 million seed round, led by specialist insurance sector investor MTech Capital. The oversubscribed funding round brings the total raised by the company to $6.8 million, following a $2 million pre-seed round last March. Additional investors in the seed round included Sure Valley Ventures, Portfolio Ventures, Ventures Together, and other angels, with European early-stage venture capital Project A Ventures also participating on a super pro rata basis.

Nettle's platform leverages AI to automate insurance inspections, significantly speeding up the process by up to five times. The AI workspace integrates remote risk identification, guided inspections, mobile evidence analysis, and instant report generation. It standardises data collection, automatically analyses it, and generates risk scores for all stakeholders.

In 2024, the London-based start-up was founded by co-founders Jack Miller and Katya Kinane, who met while working at McKinsey's AI arm QuantumBlack. Kinane, a Trinity College Dublin graduate in computer science, played a pivotal role in building Nettle's platform.

The recent funding will be used to expand Nettle's operations across the US and Europe, as well as to bolster its engineering and go-to-market teams. The company already has offices in New York. According to Miller, the platform enables top global insurers to turn loss control into a competitive advantage, with minimal integration effort required.

Kevin McLoughlin, founder and managing partner at MTech, commented that Allianz Törkiye has adopted the Nettle platform following a successful pilot program, which demonstrated a threefold improvement in the speed of risk engineers completing property inspections.

Written by urgent.news from Silicon Republic's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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