Clinical teams need timely awareness without moving sensitive patient data into external systems. CGC Sentinel processes camera and sensor feeds near the point of care, creates compact events, and routes operational signals into clinical workflows through Nexus Agents.
The Challenge
Modern healthcare environments increasingly depend on connected medical devices, wearables, monitoring cameras, mobile devices, gateways, and bedside sensors. These systems can generate signals from movement, posture, heart activity, blood pressure, glucose, temperature, oxygen saturation, and other patient indicators.
Many IoMT devices are resource-constrained, so cloud-first architectures often become the default for heavier AI processing. That creates practical problems in clinical settings: latency, bandwidth cost, intermittent connectivity, and privacy risk around sensitive health data.
The CGC Product Fit
CGC Sentinel acts as the edge perception layer for room-based monitoring, fall detection, activity recognition, and visual safety events. It can run on single-board computers, gateways, or local edge servers close to the data source.
Nexus Agents provides the orchestration layer: triaging high-confidence events, checking clinical context, suppressing noisy alerts, and escalating the right information to care teams. Forge LLM supports model lifecycle operations, including evaluation, version tracking, and safe updates for models deployed into constrained edge environments.
The Edge-to-Cloud Continuum
The architecture does not force all computation into one place. Source devices collect patient signals. Edge nodes perform low-latency inference and local decision support. Cloud or central systems are used for fleet-level monitoring, retrospective analytics, model evaluation, and governance when sensitive raw data does not need to move.
This keeps immediate clinical decisions close to the patient while still allowing the organization to improve models, track performance, and manage deployments across departments or facilities.
Anomaly Detection & Alert Routing
The same pattern can extend beyond vision-based fall detection. Wearable or bedside signals can be used for anomaly workflows, such as unusual activity levels, heart rhythm irregularity indicators, deterioration risk, or sensor patterns that deserve clinician review. The goal is not to replace clinicians; it is to reduce the time between signal, interpretation, and action.
The Outcome
The architecture reduces raw data movement, supports faster response, lowers bandwidth dependency, and gives clinical teams a clearer operating model for privacy-preserving AI. Patient-facing intelligence happens near the patient; governance, improvement, and reporting happen across the wider system.