Digital platform for waste management and circular economy operations
SYNQONY is a German waste and environmental management software company founded in 2023, now operating at 51–200 headcount. The tech stack reveals a company pivoting toward AI-driven operations: they're running Python + OpenAI + Azure AI + LangChain + LlamaIndex, paired with a conventional backend (C#, SQL Server, ASP.NET) and modern cloud infrastructure (Azure, Kubernetes, PostgreSQL). Active hiring is concentrated in engineering (4 roles) with support and product backfill, signaling they're scaling API and microservices delivery while managing a complex legacy ERP landscape they're simultaneously modernizing.
SYNQONY provides domain-specific software for waste management, environmental compliance, and circular economy operations across commercial waste producers, municipal disposal operators, and environmental authorities in Germany. The platform connects specialized workflows for waste classification, tracking, and regulatory reporting. Internally, the company is executing a significant modernization program: migrating from a fragmented ERP environment to a unified data model, containerizing workloads on Azure Kubernetes, and building a central REST API to unify downstream systems. The engineering organization is mid-stage, balancing this modernization debt against product velocity.
Python, OpenAI, Azure AI, LangChain, LlamaIndex, C#, SQL Server, React, TypeScript, Azure, Kubernetes, PostgreSQL, and ASP.NET. The stack blends AI/ML tooling (LangChain, RAG, LlamaIndex) with cloud infrastructure (Azure, Kubernetes) and traditional enterprise backends.
A modernized platform centered on a group-wide data model, REST API expansion, and microservices architecture on Azure Kubernetes. Parallel projects include ERP system development, improved development processes, and internal support optimization.
SYNQONY GmbH'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.