7IM is a UK-based investment manager serving advisers and their clients through discretionary portfolio management and multi-asset funds. Their tech stack is almost entirely Azure-native (DevOps, Container Apps, Front Door, API Management, Power Platform) with C#, SQL Server, and Databricks for core services—a consolidation strategy evident in their modernization roadmap, which lists legacy cloud migration, CI/CD automation, and traffic flow optimization as active priorities. Hiring velocity is accelerating across engineering, support, and operations, suggesting a push to automate internal workflows and reduce operational friction.
Seven Investment Management (7IM) was founded in 2002 and operates from London as a privately held investment management business. The company serves individuals, families, and financial advisers through three main product lines: discretionary portfolio management (where 7IM makes investment decisions directly), risk-profiled multi-asset funds, and model portfolios designed to match different risk appetites. They also maintain an in-house platform (the 7IM Platform and 7IMagine) that advisers use to access global securities and manage client relationships. The business operates at 201–500 employees and is currently executing a technology modernization effort, migrating to Azure cloud infrastructure and improving operational systems.
7IM's primary stack is Azure (DevOps, Container Apps, Front Door, API Management, AD), with C#, SQL Server, Python, Databricks, and Power Platform (Power Apps, Power Automate, Power BI) as core tools. They also use Bloomberg Terminal, Terraform, and test automation frameworks (Playwright, Selenium, SpecFlow).
7IM is focused on cloud modernization: Azure platform components, CI/CD pipeline development, container orchestration via Azure Container Apps, and traffic management via Front Door and API Management. They're also building CRM nurture journeys and digital lead flows to improve client engagement and conversion.
7IM'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.