- Tentris secured €2 million in pre-seed funding, beginning with a German government grant before attracting venture capital.
- Bloomhaus Ventures led the private investment, joined by Vanagon Ventures, āltitude, 10x Value Partners, and angel investor Pascal Wichmann.
- The German graph database startup says its technology can efficiently handle complex, circular data queries that usually slow down other systems.
Tentris, a graph database startup from Paderborn University, raised €2 million in pre-seed funding to help enterprise I systems query connected data without the performance problems typical of graph databases.
Bloomhaus Ventures, a Swiss firm that invests in early-stage startups in the DACH region, led the private capital round. Vanagon Ventures, āltitude, 10x Value Partners, and angel investor Pascal Wichmann also participated.
“Our mission is to build a trusted graph database for the next generation of enterprise AI. Knowledge graphs provide the context AI needs, but today’s graph databases struggle to keep up as data grows,” Alexander Bigerl, co-founder and chief executive of Tentris, tells Tech Funding News.
This funding arrives as companies look for ways to make AI systems easier to explain and less likely to give wrong answers. The graph database market was valued at $3.31 billion in 2025 and is expected to reach $11.35 billion by 2030, with a compound annual growth rate of 27.89%. This trend matches the rise in AI infrastructure investments reported by TFN this year.
Seven years in the making
Bigerl started Tentris in 2024 with Tobias Rebert, Nikolaos Karalis, and Professor Axel-Cyrille Ngonga Ngomo. The idea began about seven years earlier, during Bigerl’s studies, when he explored using tensor computation techniques to speed up graph databases. This became his PhD thesis and later the basis for the company.
“This is much too good to be left in the drawer, like so many other research results,” Bigerl tells TFN about deciding to commercialise it.
Headquartered in Herford, Germany, the startup uses a compressed data structure called a hypertrie to represent knowledge graphs. This targets a specific technical problem: queries that trace a loop between two connected records, for example, finding customers who both bought and reviewed a product, which conventional systems struggle to process efficiently.
“These cyclic connections, they seem pretty simple in the human mind, but for computers and databases, they are a big obstacle, because they need tremendous amounts of resources to compute,” Bigerl adds.
Tentris says these types of queries run just as fast as any others on its system, while other systems often slow down or stop. For example, a bank tracking suspicious transactions across millions of accounts could run that query in real time instead of using batch processing.
Bigerl lists Graphwise’s GraphDB and US-based Stardog as Tentris’s main competitors, positioning the startup in a more specialised segment of the graph database market rather than competing with larger companies like Neo4j.
A grant came before the venture capital
Unlike most startups that raise private capital early, Tentris built its first product before seeking investors. The company started with Germany’s EXIST Transfer of Research program, a federal grant for university spinouts. This funding helped them build a beta version and gather early customer feedback. Bigerl called the feedback overwhelmingly good and said it helped attract later venture capital.
“Tentris combines breakthrough graph database technology with a market poised for exponential growth, driven by the adoption of enterprise AI and the increasing need for structured, explainable data infrastructure. Built on years of world-class research and protected by a deep technical moat, Tentris has already demonstrated strong performance advantages and early enterprise demand, positioning it to become a foundational layer for enterprise AI applications,” adds Tim Schwichtenberg, senior investment manager at Bloomhaus Ventures.
The new funding will help grow the development team, with plans to expand sales later. The company has about 10 employees and is currently handling sales directly through its founders. They plan to hire someone for community management before building a dedicated sales team.
On the product side, Tentris is working on a feature that will let customers query older versions of their data without losing performance. This feature is expected after the 1.0 release later this year.
Whether early customer goodwill can convert into paying enterprise contracts before the next funding round comes due will say more about Tentris’s prospects than the size of this one.
