Low-code process automation platform with built-in AI orchestration
Bizagi is a cloud-native low-code platform centered on process automation and AI. The stack spans .NET, Angular, Azure, and SQL Server—a mature enterprise Microsoft ecosystem—alongside Python and Java, indicating active data and ML work. Current priorities reveal tension between legacy and future: cloud migration is active (listed as both project and pain point), and the org is grappling with AI ROI foundations while racing to capture demand for AI-powered process automation. Engineering-heavy hiring (7 of 15 open roles) in Colombia and Germany suggests scaling execution on these migrations.
Bizagi provides a cloud-native platform for business process automation and low-code application development, targeting enterprises undergoing digital transformation. The product unifies process design, system orchestration, and AI assistance to help organizations modernize workflows across multiple systems. Founded in 1989 and based in Washington DC, the company operates as a privately held firm with 201–500 employees. Current operational focus spans cloud infrastructure migration, document and financial data integration, and internal process automation—signaling both product maturation and internal scaling challenges.
Bizagi's core stack is .NET and Azure for cloud infrastructure, with Angular and C# for application development, SQL Server for data, and Python and Java for backend services. The platform also integrates with SAP, Workday, and other enterprise systems via SOAP and API connectors.
Active projects include cloud migration of the product, document management, custom reporting, and internal process automation. Pain points reveal focus on AI ROI foundations, seamless system integrations, and optimizing financial data flows—suggesting product roadmap priorities around AI-powered automation and enterprise integration.
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Bizagi'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.