A real-time, edge-native AI platform for healthcare. It brings together triage, patient monitoring, and diagnostic intelligence into a single system, running locally on compact hardware with sub-millisecond inference.
At a platform level, Apollo Edge integrates the full AI lifecycle — from data ingestion and pre-processing, through model training and optimisation, to real-time inference at the edge. We support multiple data modalities including medical imaging, time-series signals, and structured clinical data. Each model is optimised using TensorRT or lightweight inference engines to achieve low latency, typically under 2 milliseconds, enabling real-time decision support without relying on cloud infrastructure and data RTTs over the WAN.
The Radiology module performs multi-label chest X-ray classification using a DenseNet-121 model trained on over 100,000 images. The Remote Monitoring module processes ECG waveform data using a 1D CNN to classify cardiac rhythms in real time. The Ambulance module provides real-time triage decision support using a LightGBM model trained on emergency department data.
Apollo Edge demonstrates how real-time, multi-modal AI can be deployed at the edge to improve clinical outcomes, reduce latency, and operate independently of the cloud.
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