AI-guided claims platform for disability and workers' compensation
EvolutionIQ builds explainable AI tools that guide claims professionals toward better outcomes in disability and workers' compensation. The stack is ML-forward (scikit-learn, Keras, PyTorch, Vertex AI on GCP) paired with modern data infrastructure (Dagster, BigQuery, Spark), and the project roadmap signals a major shift: transitioning from batch data pipelines to event-driven agentic systems while launching into auto casualty claims. Leadership hiring (3 directors, 3 senior roles) across product and engineering reflects scaling the AI platform and expanding product scope.
Notable leadership hires: Product Director
EvolutionIQ is an AI claims guidance platform owned by CCC Intelligent Solutions, operating across group disability, individual disability, and workers' compensation markets. The company employs over 200 staff distributed across the United States, Europe, and Australia. The core product delivers explainable AI recommendations and Next Best Action guidance to claims professionals, aiming to improve outcomes for claimants, carriers, and end customers. Active infrastructure work includes data onboarding systems, event-driven architecture migration, and tooling to accelerate internal SDLC with AI.
React and Node.js for frontend; Python, scikit-learn, Keras, and PyTorch for ML; PostgreSQL and BigQuery for data; GCP infrastructure with Kubernetes, Terraform, and ArgoCD for orchestration; Dagster and Spark for data pipelines; Salesforce and Zendesk for operations.
Launching an AI-first product for auto casualty claims; migrating from traditional batch pipelines to event-driven agentic systems; building scalable data onboarding and validation solutions; and developing AI-powered tools to accelerate internal SDLC.
EvolutionIQ'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.