EdgeLens AI helps smart-home and networking OEMs transform routers, cameras, and in-home sensors into on-device AI agents that understand presence, learn daily rhythms, and surface meaningful changes for families and caregivers — with raw video and sensor streams kept local by default.
We sell to the OEMs who ship the hardware — and we are designed around the people living with it.
Turn installed routers, gateways, and cameras into a premium, privacy-first home-awareness subscription — no cloud video, no new hardware, no NPU dependency.
Families and caregivers get quiet peace of mind — knowing a parent is okay without cameras in private spaces, wearables to forget, or a stream of clips to watch.
The hard part isn’t a cloud demo. It’s private, reliable, low-cost inference on devices with tight CPU and memory budgets. EdgeLens is CPU-first by design, with on-demand multimodal verification — so the device’s primary job is never at risk.
Designed to port across router, camera, and hub silicon families via a thin integration layer.
A private, on-device home-awareness layer for aging-in-place. Guardian learns a home’s normal rhythm in days, then surfaces calm, graded signals only when something genuinely changes — routine drift, prolonged inactivity, or a bathroom overstay.
Presence, home/away, daily-activity summary, and privacy zones.
Behavioral baseline, meaningful-change & inactivity alerts, caregiver notifications.
Fall-like event escalation with on-demand multimodal verification.
EdgeLens Guardian is a home-awareness and wellness product. It is not a medical device or emergency-response system.
Presence, occupancy, and 7-day behavioral baselines from the always-on router — covering rooms cameras can’t, pinned to a single CPU core.
Purpose-built, quantized EdgeLens NanoML models tuned to run in real time on constrained home silicon.
Camera/radar fusion triggers only for higher-confidence events, then idles — protecting privacy and the device’s primary job.
On-device small models can finally run in real time on constrained hardware. IEEE 802.11bf-2025 standardized Wi-Fi sensing. Aging demographics and a caregiver crisis make private home awareness urgent — while rising cloud-AI costs squeeze OEM margins. The teams that embed on-device intelligence in this window will define the category.
EdgeLens AI builds privacy-first, on-device ambient intelligence for the connected home. Our software turns routers, cameras, and in-home sensors into local AI that understands a home’s daily patterns and surfaces meaningful changes, without sending raw home data to the cloud. We sell to smart-home and networking OEMs.
The buyer is smart-home and networking OEMs — router, gateway, camera, and hub makers. The end beneficiaries are families and caregivers monitoring aging parents.
Cloud camera AI streams video off-device — adding cost, latency, and privacy risk — and it can’t cover private spaces. EdgeLens runs on-device, uses Wi-Fi sensing to cover those spaces, and keeps raw video and sensor streams local by default.
No. Raw video and sensor streams stay local by default. EdgeLens sends derived context, summaries, and authorized caregiver alerts — not raw private home data.
It is designed for CPU-first baseline sensing on constrained home silicon (router, camera, hub families), with optional camera/radar verification where device resources allow.
“The next wave of home AI can’t stay tethered to the cloud. We’re putting that intelligence in the room — quietly watching for what matters, for the people you love.”
Engaging select smart-home, networking, and AgeTech OEMs for paid design-partner evaluations.