Tentris Raises 2 Million Euros

While companies around the world are investing billions in artificial intelligence, AI systems are often unable to reliably access existing corporate knowledge. Deep-tech startup Tentris aims to solve this bottleneck. The startup, which originated at the University of Paderborn, has now secured a total of approximately two million euros in funding.

The funding consists of a pre-seed round of 925,000 euros led by Bloomhaus Ventures, as well as additional investments from Vanagon Ventures, āltitude, 10x Value Partners, and business angel Pascal Wichmann. In addition, the company has received approximately one million euros in public funding from the EXIST Research Transfer program of the Federal Ministry for Economic Affairs and Energy.

When AI Falls Short with a Company’s Own Data

As AI applications become increasingly widespread, it is becoming clear that powerful language models alone are not enough. What is crucial, rather, is the ability to access structured, interconnected, and trustworthy corporate data.

This is where so-called knowledge graphs come into play. From semantic search and AI agents to Retrieval-Augmented Generation (RAG) and intelligent knowledge management, they form the foundation for numerous modern AI applications. However, existing graph databases often reach technical and economic limits when dealing with large and complex data structures.

Eight Years of Research as a Technological Foundation

The company’s technology is based on more than eight years of research at the University of Paderborn. The team has developed a new generation of graph databases specifically designed for highly interconnected enterprise data and modern AI applications.

According to the company, its proprietary architecture enables queries to be executed up to 150 times faster than with conventional graph databases. At the same time, storage requirements are said to be reduced by up to a factor of 1,000. This enables complex analyses and real-time queries that were previously hardly economically feasible due to high costs or technical limitations.

TentrisDB as the Infrastructure for the Next Generation of Enterprise AI

At the heart of the technology is TentrisDB, a high-performance, disk-based RDF graph database. It is based on a proprietary data architecture that has been specifically optimized for analytical workloads and large knowledge graphs. For the first time, companies will be able to analyze their scattered data sources in real time and make them usable for AI applications.

Our mission is to develop the trusted graph database for the next generation of enterprise AI.

Alexander Bigerl, CEO of Tentris

Existing technologies have been able to keep pace with the growth of modern knowledge graphs only to a limited extent.

Major companies are already testing the technology

According to the company, the technology is already being tested by several Fortune 500 technology companies that are reaching performance and scalability limits with existing graph databases. In addition, Tentris is already working with its first enterprise customers and, according to the company, has a growing pipeline of medium-sized and large companies that are currently evaluating the technology.

The newly raised capital will now primarily be used to further develop TentrisDB, expand the engineering and sales teams, and accelerate the time to market.

Europe’s Chance for Its Own AI Infrastructure

Investors see Tentris not only as a technological innovation but also as a strategic opportunity for Europe as a technology hub. As knowledge graphs increasingly become critical infrastructure for modern AI systems, the question arises as to whether Europe will develop these key technologies on its own or become entirely dependent on international providers in the future.

Tentris’s strengths lie not only in its scientific foundation but also in its growing recognition within the professional community: Just recently, the startup won first place at the international Knowledge Graph Conference in New York, where it was honored by industry experts.

The coming years should reveal whether graph databases will indeed become one of the central infrastructure components for enterprise AI and whether a startup from Paderborn can play a decisive role in this development.