Privacy-preserving AI for skilled nursing facilities. 3D LIDAR and a V-JEPA world model detect and predict falls in real time — no cameras, no wearables, no identifiable patient data.
Every fall in a care facility is a failure of awareness — not of the staff who work tirelessly, but of the systems they rely on. We built Prevera because we believe prevention must come before reaction, and awareness must be continuous, not occasional.
Our AI doesn’t wait for a fall to happen. V-JEPA world models predict gait trajectory changes 2–3 seconds before a fall occurs, giving care teams time to intervene.
24/7 privacy-preserving LIDAR monitoring creates continuous spatial awareness of every resident — without cameras, without wearables, without compromising dignity.
In skilled nursing facilities, falls drive regulatory penalties, litigation exposure, and heartbreaking patient outcomes. Current solutions force a trade-off between safety and privacy.
From sensor to clinical decision in under 500 milliseconds — all processed at the edge, never in the cloud.
Ceiling-mounted 3D LIDAR captures point clouds at 10–20 Hz. No cameras, no images, no PHI at the sensor layer.
Point clouds convert to bird’s-eye-view depth maps. V-JEPA 2.1 produces motion embeddings on the edge GPU.
LIDAR features merge with pressure mat readings and clinical data via multi-modal sensor fusion.
Dual-path output: instant fall detection (<500ms) plus JEPA trajectory forecasting of pre-fall patterns (2–3s).
Every design decision optimized for the skilled nursing environment — where privacy, speed, and integration aren’t optional.
LIDAR captures geometric coordinates, not images. V-JEPA operates on abstract embeddings. Zero identifiable data exists at any layer of the system.
The V-JEPA 2.1 world model learns latent representations of human motion physics — forecasting gait anomalies 2–3 seconds before ground impact.
NVIDIA Jetson Orin runs all inference locally. Sub-500ms detection latency with no cloud dependency — works even if internet is down.
HL7 FHIR R4 native. Plugs into PointClickCare and other EHRs. Auto-populates eMAR cross-references and MDS 3.0 fall reporting.
Combines LIDAR point cloud data with pressure mat arrays and clinical risk factors for the highest-accuracy fall prediction available.
Real-time monitoring, trend analysis, and risk scoring — accessible via web and mobile. Tiered alerts by severity for staff and administrators.
Purpose-built for healthcare. Every component selected for clinical reliability, privacy compliance, and edge performance.
3D LIDAR + pressure mats. Geometric data only — no cameras, no PHI at capture.
NVIDIA Jetson Orin. BEV projection, point cloud preprocessing, local inference.
V-JEPA 2.1 world model. Motion embeddings, gait analysis, trajectory prediction.
Dashboard, alerts, FHIR integration, MDS 3.0 reporting, mobile monitoring.
Guardian+AI pays for itself by reducing the most expensive adverse event in skilled nursing — while eliminating the privacy liability of camera-based alternatives.
No cameras, no video, no identifiable data to breach or subpoena.
Falls with Major Injury measure enters SNF VBP scoring FY 2027. Get ahead now.
Auto-populates MDS 3.0 Section J, eMAR cross-references, and incident reporting.
CMS is making falls a financial metric. We’re the only privacy-preserving, predictive solution positioned for this inflection point.
Fall detection and prevention across all U.S. healthcare facilities by 2034. Growing at 5.6% CAGR driven by aging demographics.
AI-enabled fall detection in SNFs, assisted living, and rehab — facilities with the infrastructure and budget for advanced solutions.
Year 3 target: 188 facilities at $125K ACV. Conservative 1.7% penetration of SAM with land-and-expand model.
Patent pending. Path A: $1.5M raise for MVP, 3 pilot facilities, and first commercial contracts within 12 months.
Privacy-first architecture simplifies compliance from day one.
No identifiable data captured or stored. Privacy by architecture, not by policy.
Qualifies for Clinical Decision Support Non-Device exemption under January 2026 guidance.
Directly addresses Falls with Major Injury measure entering SNF VBP scoring FY 2027.
Edge-first architecture minimizes attack surface. SOC 2 Type II certification on roadmap.
Real-time fall monitoring, predictive alerts, and care team communication — all from your phone. Our mobile app is currently in development.
Want a sneak peek? Explore our interactive prototype below.
Whether you’re a facility looking to protect residents or an investor seeing the regulatory inflection point — let’s talk.