Pie Insurance vs Akur8
Side-by-side comparison of AI visibility scores, market position, and capabilities
Data-driven workers' comp for small businesses; raised $300M+; ML underwriting enables fast quotes through digital brokers; one of the largest insurtech workers' comp carriers in the US.
About
Pie Insurance is a Washington, D.C.-based insurtech company that provides workers' compensation insurance to small and medium-sized businesses through a technology-enabled platform. Pie uses alternative data sources and machine learning to underwrite workers' comp policies more accurately than traditional actuarial models, enabling it to offer competitive pricing to businesses in industries like construction, restaurants, and healthcare services. The platform delivers quotes in minutes through a digital flow that connects directly with insurance brokers and agents, who remain central to Pie's distribution strategy. Founded in 2017, Pie has raised over $300M from investors including Accel, Greycroft, and Moxley Holdings and has grown to become one of the largest insurtech workers' comp carriers in the United States. Pie's model captures the small business segment that is often underserved or overpriced by traditional carriers who rely on limited data for these accounts.
Akur8 is the leading AI actuarial pricing platform for insurers with 300 customers across 40+ countries; grew ARR 50%+ in 2025; acquired Matrisk in Jan 2026 to expand its market intelligence capabilities.
About
Akur8 is a Paris-based insurtech company that provides AI-powered pricing and reserving software for non-life insurance companies. Founded with the goal of bringing transparent machine learning to insurance actuaries, Akur8''s platform enables underwriters to build pricing models faster, more accurately, and with full auditability — a key differentiator in a heavily regulated industry where black-box ML cannot be deployed. Its proprietary Transparent Machine Learning (TML) approach makes the model logic explainable to actuaries and regulators alike.
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