Overview

Modern robotics developers are forced to work with a fragmented AI stack. Building real-time autonomy means combining libraries like OpenCV, PyTorch, TensorFlow, and ROS perception modules, managing hardware-specific dependencies, and connecting brittle APIs.

Even when using ROS, adding AI features such as perception or natural language understanding often depends on cloud services, remote inference endpoints, or custom pipelines. Local deployment is possible, but usually requires deep ML expertise and careful optimization. For most teams this adds latency, complexity, and points of failure that slow iteration and limit secure or offline use cases.

QACE closes this gap by delivering robotics-native APIs across core AI modules such as perception, interaction, and decision-making. Instead of stitching together generic AI services, developers get endpoints designed specifically for robotics workflows, making it as simple as plugging into ROS topics or hardware drivers.

QACE delivers SDKs and local runtimes as part of its flagship edge kit, bringing perception, interaction, and decision-making directly on-device. This local-first approach gives low latency, privacy, and offline reliability for robots in factories, hospitals, or the field.

From factory environments with limited connectivity to healthcare robots requiring strict data privacy, today’s patchwork approach does not scale. QACE solves this with robotics-native APIs and a flagship edge kit built on optimized, ROS-compatible modules that deploy on edge hardware in minutes instead of weeks.