MOTOR supplies automotive data to fleets, repair shops, and industry operators—a 120-year-old business now undergoing technical transformation. The stack reveals a data-first architecture (Databricks, Delta Lake, Spark, Redshift) paired with emerging AI tooling (LangGraph) and aggressive cloud infrastructure (AWS Lambda, Fargate, Kubernetes). Active projects center on market segmentation, AI-driven research workflows, and executive dashboards, while pain points cluster around SaaS platform maturity and revenue retention—a pattern typical of legacy data vendors modernizing their delivery model.
MOTOR Information Systems is a privately held automotive data provider based in Troy, Michigan, serving fleets, repair shops, and industry operators across North America. The company has operated since 1903, building partnerships with customers at scale from national fleet operators to independent repair facilities. Recent work focuses on architectural modernization, market segmentation analytics, and AI-enabled research and account analysis capabilities. The organization is addressing revenue retention and SaaS platform sustainability as core operational priorities while maintaining its core mission of delivering timely, accurate automotive information.
MOTOR runs Python, AWS (Lambda, Glue, Fargate), Databricks, Delta Lake, Kubernetes, Redshift, Apache Spark, and Ray for data infrastructure. For AI workflows, they use LangGraph. Sales and CRM tooling includes HubSpot, Salesforce, and Salesloft; deployment via Azure DevOps and Octopus Deploy.
MOTOR Information Systems is headquartered in Troy, Michigan and is a privately held company with 201–500 employees.
MOTOR Information Systems'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.