Qontext is an AI infrastructure company building an independent context layer that enables enterprises to provide AI applications, agents, and workflows with accurate, continuously updated business knowledge. Instead of requiring every AI tool to maintain its own integrations and data pipelines, Qontext creates a centralized, reusable context layer that connects information from CRMs, documentation, messaging platforms, internal databases, product knowledge, customer records, and other enterprise systems. This allows organizations to deploy multiple AI-powered applications while maintaining a single source of truth for business context.
The platform is designed for organizations adopting AI across departments such as sales, customer support, marketing, operations, and engineering. By synchronizing fragmented company knowledge and applying permission-aware access controls, Qontext ensures AI assistants and autonomous agents retrieve relevant information without duplicating integrations for every application. The platform also supports Model Context Protocol (MCP), enterprise governance, auditability, and secure data management, making it suitable for production AI deployments where accuracy and compliance are essential.
Founded by Lorenz Hieber and Nikita Kowalski, Qontext addresses one of the biggest challenges in enterprise AI: fragmented organizational knowledge. The company believes that reliable context—not just powerful language models—is the foundation for successful AI adoption. By separating business context from individual AI models, Qontext enables organizations to build scalable, model-agnostic AI systems that deliver more consistent outputs, reduce implementation complexity, and accelerate enterprise-wide AI automation. Following its $2.7 million pre-seed round, the company is expanding its platform to become a foundational infrastructure layer for AI-native organizations.