Rethinking edge networks for an AI-driven world

Creating a new foundation for real-time AI applications

At Latos, we’re developing an entirely new approach to edge network infrastructure design with the goal of meeting the rising demands of real-time AI applications.

As AI use cases like predictive healthcare, autonomous vehicles, and streamlined public services accelerate, the limitations of conventional edge networks are becoming increasingly clear. Many simply don’t offer the processing power required to run sophisticated AI models at speed and scale.

Our innovative approach to data centre design changes that.

Inspired by our own neural networks that transmit signals efficiently between parts of the body, this new generation of edge architecture allows data to flow rapidly between users and the processing power they rely on. While remaining compact and easily embedded, it minimises latency, strengthens responsiveness, and enables the real-time action that is critical for AI-driven experiences.

At the heart of this vision are our data centres: purpose built, modular, and designed for rapid deployment. These facilities provide the power and cooling needed to support the most demanding AI workloads while remaining cost-effective and small enough to integrate exactly where they’re needed. This level of flexibility is all-new to edge computing and gives organisations the freedom to scale their capacity incrementally, growing their edge networking capabilities in line with their needs.

As countries around the world move toward a more AI-enabled future, our neural edge model offers a future-proof foundation, one that combines ingenuity, operational flexibility, and sustainability.

Read the full press release [here].