Frontier AI research, safety-critical systems, and deep insurance operating experience. That mix lets Kinro ship product fast in a regulated market without cutting corners.
Pierre previously worked as an AI researcher at Google DeepMind, where he helped improve Gemini for financial services. He focuses on building reliable AI systems for regulated domains through rigorous evaluation and fine-tuning, and previously completed a PhD at Meta FAIR after a master's at Oxford.
Parth previously led infrastructure and systems work across training and inference at Zoox. He specializes in reinforcement learning, simulation, GPU clusters, and safety-critical real-time systems, after earlier work at Samsung's Advanced Technologies Group and graduate studies in robotics at UT Austin.
Corentin was the first employee at Sharelock, an insurtech that became profitable while working with major carriers and brokers. He built insurance products and MGA risk workflows before scaling team operations, and now focuses on turning distribution complexity into a reliable customer experience.
Selling insurance is not just a conversion problem. It requires strict regulatory compliance, deep carrier integrations, and answers that remain reliable across every customer interaction.
General-purpose platforms will help users discover products, but regulated distribution needs vertical infrastructure. Kinro continuously evaluates and improves every agent to optimize sales performance while remaining 100% compliant.
Agents handle discovery, personalized recommendations, quoting, and policy binding.
We work with distribution partners operating in large regulated insurance markets.
Insurers pay more than $100B annually in commissions, while distributors spend over $10B each year acquiring customers.