Process automation platform combining AI with domain expertise for mid-market operations
Stoque builds process automation solutions for mid-to-large companies in Brazil, grounded in two decades of operational consulting. The tech stack—Azure, .NET, Python, Docker, Kubernetes, plus BPMS and AI tooling—reflects a mature platform engineered for complex, regulated workflows. Active hiring skews toward operations and support roles while the project backlog emphasizes document digitization, process mapping for financial pipelines, and procurement automation, suggesting a shift toward vertical-specific automation templates and customer success infrastructure.
Stoque develops process automation software and consulting services for Brazilian mid-market and enterprise operations. The company operates Zeev, a BPMS platform with AI capabilities, and pairs technology with on-site process optimization teams. Founded in 2002, Stoque has grown to 500+ employees across Brazil, with headquarters in Belo Horizonte. The business model combines software licensing (Zeev) with implementation and advisory services, addressing document handling, workflow redesign, and operational bottleneck elimination across finance, real estate credit, and procurement functions.
Zeev is built on .NET Core, ASP.NET, and C# for backend services, with Vue and JavaScript for frontend. Infrastructure runs on Azure (Bicep, Codex, Azure Boards) and AWS, containerized via Docker and Kubernetes. BPMS and process modeling use BPMN standards.
Key projects include document digitization, process mapping for financial and real estate credit workflows, customer retention programs, procurement automation, and performance analytics. The company is also developing Zeev branding and landing page infrastructure to support SMB market expansion.
Stoque'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.