WaveSense starts with urgent care-home ROI, then compounds into PRECOG: a longitudinal passive health dataset and predictive intelligence layer for people traditional monitoring cannot reach.
Ageing populations, dementia prevalence, staffing pressure and home-based care are all moving in the same direction. The highest-risk people need more continuous monitoring, but the current tools depend on resident action, wearable compliance, camera-only coverage, or periodic human checks.
WaveSense turns the room itself into a passive sensing layer. The wedge is obvious operational value: falls, night safety, vital signs, deterioration signals, audit evidence and fewer disruptive checks. The bigger prize is predictive health intelligence built from real-world longitudinal care data.
Care homes understand falls, missed night deterioration, CQC evidence, family reassurance and stretched staff capacity.
Dementia, Alzheimer's, SEND, non-verbal care, supported living, homecare and hospital-at-home share the same monitoring failure mode.
Every deployment improves the dataset, baselines and predictive models. The product gets harder to copy as the network grows.
WaveSense enters through UK residential care, then expands into homecare, supported living, dementia care, hospital-at-home and the US long-term care market.
| Expansion zone | Why it matters | WaveSense angle |
|---|---|---|
| UK care homes | Immediate buyer universe with falls, nights, compliance and workforce pressure. | Hardware plus SaaS pilots, room-level monitoring, CQC evidence and staff workflow integration. |
| Dementia and memory care | Residents often cannot self-report, remember, press buttons, wear devices or tolerate intrusive checks. | Passive sensing with personal baselines and earlier prompts for routine, sleep, movement and vital-sign changes. |
| Homecare and supported living | Care is moving outside facilities, but solo living leaves long gaps between visits. | The same device model extends into private homes through agencies, commissioners and family-funded care. |
| US long-term care | Venture-scale category with nursing homes, assisted living, memory care and home healthcare. | PRECOG creates a platform story beyond hardware: longitudinal data, predictive models and care workflow intelligence. |
Prove falls, night monitoring, passive vitals, dashboard workflow and evidence capture in real settings.
Focus where buttons, wearables and self-reporting fail most obviously, and where early intervention is easiest to justify.
Move from facilities to distributed care as proof, hardware reliability and predictive models mature.
Use PRECOG to identify earlier signals of risk, deterioration and care-plan change across large populations.
The near-term product helps carers act earlier. Over time, PRECOG makes WaveSense more valuable with every room, every baseline, and every care response captured by the platform.
It starts with practical early-warning prompts and matures into condition-specific risk models as deployment depth grows. The result is not just more alerts, but better context for care decisions.
Continuous, room-level health and behaviour signals from vulnerable populations, captured without wearables or resident action.
The useful signal is the delta from the person, not an average threshold. That makes each deployment more clinically and operationally useful.
Alerts, care notes, evidence trails and staff response loops connect sensor data to what carers actually do next.
More rooms means more labelled change, more outcome data, better risk models and a clearer reason for operators to standardise on the platform.
We are using early pilots to validate care workflows, harden the hardware stack, and build the first PRECOG foundation dataset.