Qargo builds a SaaS transport management platform for road transport companies, with a modern stack spanning Python, Django, React, Kubernetes, and GCP. The tech mix—PyTorch, Hugging Face, LangChain, and active chatbot/document-extraction projects—reveals an engineering org shifting toward AI-assisted automation of logistics workflows. Hiring velocity is accelerating across engineering and sales, with a near-even split between customer-facing scaling (support, sales roles) and product work, indicating a company balancing customer success against feature velocity.
Qargo provides a transport management system designed for road logistics operators. Founded in 2020 and based in London, the company focuses on reducing administrative overhead for transport teams while improving operational efficiency and financial outcomes. The platform spans core logistics workflows—planning, invoicing, document handling, and customer integration—and is implemented on a modern cloud-native infrastructure. Qargo operates across the United Kingdom, Germany, Belgium, Netherlands, and the United States, with 51–200 employees and 25 open roles as of the last 30 days.
Qargo runs on Python, Django, and PostgreSQL for backend services; React and React Native for frontend; Kubernetes and GCP for infrastructure; and Hasura, GraphQL, and Firebase for data and API layers. The stack also includes PyTorch, Hugging Face, and LangChain for AI features.
Active projects include support workflow automation, customer onboarding journeys, document extraction and custom editors, chatbot development, automated bookings, and integration onboarding cycles—reflecting a focus on automating manual logistics tasks and accelerating customer adoption.
Qargo TMS'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.