Power
Overnight operation with acceptable charge cycles — continuous sensing cannot be an afterthought on the energy budget.
Case study · Client anonymized — published with approval
AI sleep system — intelligence under severe constraints
An on-device sleep intelligence platform where comfort, milliwatt budgets, and acoustic signal quality all had to win at once.
The body is the harshest product environment.
Sleep hardware fails when engineering optimizes for the lab instead of the night: heat, pressure points, battery anxiety, and models that only work with pristine signals. This program forced intelligence, acoustics, and industrial design into one envelope.
Program outcomes
- Integrated architecture established across sensing, edge inference, power, and enclosure comfort.
- On-device processing path defined within a power envelope compatible with overnight wear.
- POC success criteria written against signal quality, latency, and user comfort — not feature theatre.
The problem
Real-time insight without ruining sleep.
Consumer health products that claim AI often push work to the phone and drain trust. The brief required meaningful inference near the user — with comfort and battery life treated as non-negotiable product requirements.
Constraints
Four limits that defined the architecture.
Comfort
Thermal, mechanical, and acoustic presence had to disappear into the product experience.
Signal quality
Models are useless if the sensor chain is noisy under real sleeping conditions.
Edge compute
Inference that fits memory and latency budgets without a always-on high-power path.
Approach
Architecture first, models second.
We locked the physical and electrical envelope before model ambition: sensing chain, compute class, duty cycle, and what must remain local versus cloud. That order prevented an AI demo that could never ship as hardware.
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