VectorTree, Videntifier’s Subsidiary, Secured EU Grant to Develop Advanced Vector Database for AI

Videntifier is proud to announce that its Lithuanian subsidiary, VectorTree, has secured funding from the European Union for the project “Building an Advanced Vector Database for Artificial Intelligence” (No. 02-048-K-0005). Supported by the European Regional Development Fund under the EU Funds Investment Programme 2021–2027, the project has a total budget of €943,052.28, with €565,831.38 provided by the EU. Development is underway and will continue through Q1 2027. To support the initiative, the team will be expanding. The first customer deployments are expected by the end of this year.

The project aims to build a high-performance Advanced Vector Database (AVD) for AI as a cloud-based SaaS solution, based on Videntifier’s proven NV-tree algorithm.

As AI systems increasingly process vast volumes of unstructured data—such as images, video, text, audio, and sensor input—they require scalable infrastructure. With the EU’s support, we’re building a vector database that not only scales, but also changes how data for AI is retrieved, increasing accuracy without relying solely on exact matches,” says Ari Kristinn Jónsson, CEO of VectorTree.

AVD will meet the infrastructure need by storing vector embeddings—structured numerical representations generated by AI models from raw data—and enabling rapid, large-scale nearest neighbor search across billions of vectors. Queries will be matched against stored embeddings to retrieve highly relevant results instantly.

There is a wide range of applications: video platforms could recommend content based on visual style and pacing; legal tech firms could identify relevant precedents expressed using different terminology; and healthcare AI could surface similar historical cases to improve diagnostic speed and accuracy.

Unlike most vector databases, which retrieve only individual vectors, VectorTree’s AVD will introduce a breakthrough—vector clustering, enabling the insertion and retrieval of entire clusters of related vectors in a single operation. In healthcare, for example, clusters of vectors can represent related information—such as data from a patient or patient group—enabling AI to retrieve multiple pieces of relevant information in a structured way from close matches to query data. The result is faster, more accurate, and context-aware conclusions by AI.

More characteristics that will set VectorTree’s AVD apart is its ability to scale far beyond current market offerings. While most vector databases handle only a few billion vectors, AVD will be designed to store tens to hundreds of billions of vectors with no performance loss. Built on the proven foundation of Videntifier’s legacy NV-Tree technology—which already handles hundreds of billions of vectors with no degradation in performance and is widely recognized as a leader in fingerprint-based visual similarity search—VectorTree AVD inherits its robustness, reliability, and unmatched efficiency.

As the vector database market grows from $1.3 billion today to $8.3 billion by 2032, VectorTree is well-positioned to lead with a faster, smarter, and more versatile platform tailored to the needs of AI developers, researchers, and enterprise users.

Videntifier’s technology is already trusted by multiple law enforcement agencies, including INTERPOL, as well as large-scale social media platforms such as Meta, where its visual search capabilities have proven effective at internet scale.