Placement and underwriting platform for specialty insurance brokers and carriers
Artificial Labs builds software that digitises specialty (re)insurance placement for brokers and scales algorithmic underwriting for carriers. Its Haskell, Nix and TypeScript core on a deeply AWS-native serverless stack signals an unusually rigorous engineering culture. Accelerating, lead-heavy hiring across engineering, sales and security, plus work on a compliance register and certification readiness, points to maturing governance as pilots scale into production.
Artificial Labs is a London-headquartered insurtech founded in 2013, privately held and sized at 51–200 employees. The company serves the specialty and commercial insurance market with tooling for two sides: brokers, who use it to digitise placements and manage capacity, and carriers, who use it to apply underwriting logic across lead and follow business. Its platform runs primarily on AWS, with services spanning ECS, Fargate, Lambda and Step Functions, backed by PostgreSQL, MySQL and DynamoDB. The company is currently hiring in the United Kingdom and Germany.
Artificial Labs builds with Haskell and TypeScript, packaged via Docker and Nix, provisioned with Terraform on AWS (ECS, Fargate, Lambda, Step Functions), with PostgreSQL, MySQL, DynamoDB and OpenSearch for data.
Yes, hiring is accelerating, weighted toward engineering, sales and security, with many lead and manager-level roles. Openings are in the United Kingdom and Germany.
Current initiatives include scaling pilot projects, building platform integrations, publishing public API documentation, and establishing a compliance obligations register and certification readiness program.
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Artificial Labs'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.