Organizations exploring generative AI often find that impressive model demos do not automatically translate into reliable results in production. What determines success is the infrastructure surrounding the model. This is the focus of deepset and its open-source framework, Haystack, which are designed to help enterprises build AI systems that actually reach deployment.
From Open Source Roots to Enterprise-Grade Operations
Founded in Berlin in 2018, deepset was created by Milos Rusic, who recognized early that reliable AI requires orchestration, evaluation, and governance. He leads the company’s mission to help enterprises integrate AI responsibly and at scale.
Haystack supports Retrieval-Augmented Generation (RAG), agentic RAG, AI agents, enterprise search, intelligent document processing, and text-to-SQL. Teams use it to assemble compound systems that connect LLM applications with their internal data and tooling. These projects are designed for practical use rather than laboratory-style demonstrations, and they meet the needs of enterprise builders who want predictable behavior.
Architecture Built for Practical Demands
Haystack uses a modular structure and remains model-agnostic, which allows organizations to choose the right LLMs and components for their environment. This flexibility also supports strong security and compliance postures.
The framework includes robust retrieval capabilities, transparent pipelines, and evaluation tools that help teams reduce hallucinations. Haystack Enterprise extends these features with governance controls, lifecycle management, access policies, and deployment choices that include cloud, VPC, on-premise, and air-gapped installations. These options support sovereign AI requirements and the needs of regulated sectors that prioritize traceability and internal oversight.
Lessons From Early Enterprise Projects
Milos’s perspective developed through hands-on work with companies such as Siemens and Airbus. Those collaborations highlighted a consistent truth: organizations need a complete system that integrates data sources, retrieval layers, workflow logic, and human review. A model on its own cannot deliver reliable outcomes, and the value of AI grows when every part of the system works in coordination.
This insight guided the development of Haystack Enterprise Platform, which provides technical teams with a structured foundation for deploying RAG and AI agents into production environments. The open-source community surrounding Haystack also contributed practical insights, helping deepset focus on reliability, clarity, and long-term maintenance.
Credibility Backed by Progress
deepset’s work has received recognition across the technology community. Milos appeared in WirtschaftsWoche’s “30 bis 2030” feature, which highlights leaders shaping the next decade of European tech. The company also earned a 2024 Gartner Cool Vendor designation in AI Engineering.
Funding rounds, a $14 million Series A led by GV and a $30 million growth round led by Balderton, supported the expansion of the company’s enterprise offerings. These milestones reflect strong interest from organizations that want dependable enterprise AI rather than short-term experimentation.
Looking Ahead
Milos aims to establish Haystack as a central standard for production AI systems, particularly in environments where AI governance, auditability, and sovereign AI deployment matter. The long-term goal is to help organizations create durable AI applications that improve knowledge work, enhance decision-making, and integrate cleanly into document-heavy operations. As more teams build RAG pipelines and AI agents, the demand for reliable orchestration continues to grow.
For technical leaders seeking reliable Retrieval-Augmented Generation, transparent AI agents, or secure enterprise search pipelines, deepset offers a path shaped by engineering discipline rather than hype. The strongest outcomes come from well-orchestrated systems, and Haystack provides the structure needed to build them.
