Cognosos
RTLS PlatformsHTMwire assessment
AI-powered RTLS that achieves room-level accuracy with ultra-lightweight infrastructure using machine learning instead of dense hardware deployments.
Key Features
- ✓ LocationAI ML engine delivers room-level accuracy with minimal hardware
- ✓ Self-healing network automatically adjusts to construction and environmental changes
- ✓ Cloud-based platform with no on-premise servers required
- ✓ Indoor and outdoor tracking from a single platform
- ✓ Subscription-based service model with professional deployment included
What It Helps You Do
What Sets Them Apart
Achieves room-level RTLS accuracy with 10x less infrastructure than traditional systems — ML replaces dense hardware, cutting deployment time and total cost of ownership.
How Cognosos Uses AI
HTMwire's independent read on the technology — not the vendor's marketing claim.
LocationAI uses ML algorithms to classify device locations at room-level accuracy from minimal BLE beacon infrastructure. The ML model continuously self-trains and adapts to environmental changes like construction, eliminating the network decay problem of traditional RTLS.
- ML room-level location engine. LocationAI uses radio-fingerprinting ML to classify a tag's room from BLE signal patterns learned during a setup/training walk, similar to image-recognition style pattern matching
- Self-healing adaptation. The model continuously learns environmental changes (construction, layout changes) to maintain accuracy without manual re-tuning
Key Numbers
- ▪ Beacons placed ~1 per 1,000-2,000 sq ft (every 30-40 linear feet)
Tags
Trust Signals
Frequently Asked Questions
What location technology does Cognosos LocationAI use?
Cognosos uses BLE tags and ceiling-mounted BLE beacons, where the tags act as receivers, plus a proprietary long-range (non-Wi-Fi, sub-GHz class) RF backhaul to relay data to gateways and the cloud. A cloud machine-learning engine, LocationAI, then infers room-level location.
How does Cognosos achieve room-level accuracy with less hardware?
LocationAI uses a radio-fingerprinting approach. During setup, staff walk tags through rooms so the system learns each room's signal pattern, building a reference network. At runtime the ML model compares live signals to learned patterns to classify room-level location. Beacons are placed roughly every 1,000 to 2,000 square feet, with no in-room hardware required.
Does Cognosos require putting equipment in every patient room?
No. Cognosos emphasizes non-disruptive installation: beacons go in ceilings about every 30 to 40 linear feet and gateways sit in existing IT closets, avoiding hardware in individual patient rooms. A self-healing network adapts to construction and environmental changes to limit accuracy decay.
Sources
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