M3 Insurance runs a tech stack spanning Salesforce, Epic Systems, and a full data pipeline (Snowflake, Redshift, Airflow, Dagster) alongside machine-learning libraries (TensorFlow, PyTorch, scikit-learn), suggesting internal analytics and risk modeling beyond typical broker operations. Sales-driven hiring (8 of 18 open roles) paired with orchestration and ML tooling indicates aggressive scaling of lead generation and underwriting automation, while active projects around workflow automation and renewal processes reveal a push to streamline operations that have historically been manual and relationship-dependent.
M3 Insurance is a private, independent insurance broker and risk management firm headquartered in Madison, Wisconsin, with 201–500 employees. The firm serves mid-market and enterprise clients across employee benefits, property & casualty, workers' compensation, and personal lines, positioning itself as a high-touch advisor with institutional-grade resources. Core service areas include risk management consulting, insurance placement, employer-sponsored benefits administration, and executive benefits planning. The company operates across multiple carrier relationships and manages complex product delivery in enterprise environments, competing on both service quality and operational speed.
M3 runs Salesforce for CRM, Epic Systems for client data, Snowflake and Redshift for analytics, and orchestration tools (Airflow, Dagster). The stack also includes machine-learning libraries (TensorFlow, PyTorch, scikit-learn) and cloud infrastructure on AWS.
Active projects include workflow automation, new business lead generation, renewal process optimization, carrier relationship management, and email automation. Pain points center on lead generation, client retention, operational efficiency, and adapting to evolving benefits and commercial insurance landscapes.
M3 Insurance'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.