V-Nova is rethinking the data layer for vision AI. Faster, more accurate AI is not a compute problem. It is a data problem.
Most pipelines still process full images and video streams for tasks that only need a fraction of the data. V-Nova’s compute-aware data formats with hierarchical access change that. AI systems can access exactly what they need, when they need it.
The result is faster, more accurate AI with less data, less compute, and lower power. At the edge, this enables real-time analytics, scalable multi-model processing, and next-generation intelligent vision systems.
V-Nova
Recent Content by Company
The Next Frontier in AI Is Not Just Reasoning. It Is Knowing When to Look Again
May 20, 2026
Why AI Metacognition Requires Hierarchical Random-access Data This blog post was originally published at V-Nova’s website. It is reprinted here with the permission of V-Nova. Executive Summary (TL;DR) Today’s AI systems can reason impressively, but they still struggle to know when to look again. Humans use metacognition as a feedback loop between thought and perception: […]
MPEG-5 LCEVC: A practical shift for industrial AI video pipelines
April 15, 2026
This blog post was originally published at V-Nova’s website. It is reprinted here with the permission of V-Nova. In Industrial and Defense environments, I hear the same story. More cameras. Higher resolutions. Stricter latency targets. Infrastructure that cannot be replaced easily. And increasing pressure around storage, bandwidth, compute, and privacy. This is why MPEG-5 LCEVC is becoming even more relevant. It improves compression […]
