MotorK operates an AI-first SaaS platform serving the automotive retail ecosystem across 8 European countries. The tech stack reveals an ML-heavy engineering organization: TensorFlow, PyTorch, scikit-learn, XGBoost, SageMaker, and Kubeflow sit alongside core infrastructure (Kubernetes, Terraform, AWS). Active projects show a shift toward event-driven architecture and automation—lead generation, CRM integration, and predictive maintenance—while pain points around legacy integration, microservices scaling, and high-traffic availability suggest they're managing rapid product evolution across dealer and OEM customer bases.
MotorK is a publicly listed automotive software company (Euronext Amsterdam) headquartered in Milan, operating across France, Belgium, Italy, and Germany. The platform serves dealer groups and OEMs, connecting marketing, sales, data, and operations through a unified SaaS layer. Core capabilities include customer acquisition, conversion optimization, and retention, powered by AI-driven workflows and analytics. The company operates at scale—6,000+ dealer groups and dozens of OEMs—and invests approximately one-third of annual revenue in R&D, maintaining one of Europe's largest tech-automotive research teams.
MotorK's core stack spans Python, Java, PostgreSQL, MySQL, and Apache Spark for data processing, with TensorFlow, PyTorch, scikit-learn, and XGBoost for ML workloads. Infrastructure runs on AWS with Kubernetes orchestration, Terraform for IaC, and Grafana/Loki for observability. Zendesk handles customer support integration.
Active projects include transitioning to event-driven architecture, developing the StockSpark platform, building CRM and marketing automation products, and scaling lead generation and campaign strategies. They're also rolling out customer training programs and predictive maintenance capabilities for dealer operations.
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