AI-powered underwriting platform for Lloyd's of London brokers
Ki builds algorithmic underwriting software for Lloyd's of London brokers, combining proprietary risk models with machine learning to automate pricing and placement decisions. The tech stack—Java, Python, PostgreSQL, GCP, BigQuery—supports both real-time inference (FastAPI, Flask) and analytics (Tableau, Looker, Streamlit), reflecting a company balancing production underwriting systems with continuous model refinement. Active hiring is concentrated in engineering and data roles at senior levels, consistent with the project focus on algorithmic model development, platform scaling, and recurring model validation work.
Ki operates an underwriting decision platform targeting brokers placing risk at Lloyd's of London. Founded in 2020 and based in London, the company employs 201–500 people and is backed by Fairfax Financial and Blackstone. The platform delivers instant underwriting decisions via a proprietary algorithm paired with machine learning, designed to compress the time and cost of risk placement. Ki's product surfaces three operational areas: a broker-facing decision API, an underlying algorithmic pricing engine, and supporting analytics infrastructure (BI tools and data pipelines). The company positions itself as modernizing a 335-year-old market through technology.
Ki's stack spans Java, Python, PostgreSQL, GCP, and BigQuery for core systems; FastAPI and Flask for APIs; Tableau, Looker, and Streamlit for analytics; and Okta, Intune, and Azure Entra for infrastructure. This mix reflects a mature ML and data engineering operation embedded in a regulated insurance environment.
Ki is based in London, United Kingdom, and currently hires only within the UK. The company was founded in 2020 and operates as a privately held firm.
Ki's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.