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About VectorTree

About VectorTree VectorTree is a true multivector database built to solve one of the biggest bottlenecks in generative AI: accurate, scalable memory. Most vector databases were designed for single-vector search and later adapted for AI workloads. VectorTree was built from the ground up for multivector search, enabling AI systems to preserve context, structure, and meaning without the massive computational cost. For years, VectorTree has powered some of the world’s most demanding image and video matching systems, operating at extreme scale with millisecond-level performance. Today, it is being made available as a general-purpose foundation for modern GenAI. Why VectorTree matters for GenAI Single-vector retrieval fundamentally limits accuracy. It averages meaning, loses structure, and leads to false positives, false negatives, and hallucinations, especially for long documents, complex queries, and multimodal data. Multivector representations solve this, but until now they have been too slow and too expensive to scale. VectorTree removes this trade-off: • Accurate, explainable retrieval for LLMs • Dramatically reduced hallucinations and missed matches • High-precision results with millisecond query latency • Designed for text, images, video, and face recognition VectorTree is not a research prototype. It is production-proven: • Nearly 100 billion vectors in a single customer deployment • The most demanding installation of VectorTree database has served 2 billion users and all their uploads • Ability to match 3,500 hours of video per hour on a single server • Matching against 1 billion hours of video with ~100 standard servers running VectorTree VectorTree is a true multivector database that is fast, scalable and accurate. That is exactly what AI needs to take the next steps.