Sleepme: Real-Time Bed-Temperature ML Delivered in Four Weeks

Sleepme builds smart mattress-topper systems (Dock Pro and Cube) with IoT-enabled sleep tracking — heart rate, respiration, bed pressure, environmental temperature and humidity. Over 150,000 customers sleep on them nightly. The product thesis is that real-time bed-temperature control meaningfully improves sleep quality.

01 The Challenge

Temperature control had to run in real time — and the ML infrastructure had to be someone else’s problem

Sleepme’s engineering team had already built a proof-of-concept for a temperature-optimization ML model. Turning the PoC into a production service was the next step: availability, scalability, observability, CI/CD, data quality, monitoring — all of it needed to be production-grade before the model could run against real customers’ sleep data.

The leadership did not want to build and operate that infrastructure in-house. It would pull engineering cycles away from the product roadmap, which was where Sleepme’s differentiation lived.

02 The Approach

Ship the model into production. Run the infrastructure under a managed-service contract.

Provectus opened with discovery sessions that mapped Sleepme’s existing stack, business requirements, and AI-adoption risks. The decision was clear: take the PoC to production on AWS, and deliver the ongoing operation as Provectus’ Managed AI Services engagement.

That structure kept Sleepme’s team on product. Every piece of infrastructure Provectus provisioned was also a piece Provectus would operate.

03 The Build

Amazon SageMaker training pipeline plus CI/CD plus managed MLOps

The Provectus team set up a training pipeline, handled dataset versioning, and packaged the model on Amazon SageMaker. CI/CD workflows automated release and deployment. Model observability, maintenance, and re-training were built in from the start.

On the ongoing-operations side, Managed AI Services cover infrastructure provisioning and maintenance, access management, cost monitoring and optimization, networking, compliance, disaster recovery, and support — troubleshooting, issue remediation, escalation. Sleepme engineers get a production AI pipeline without becoming an MLOps team.

04 The Results

Real-time bed-temperature recommendations in production, four weeks from kickoff

4 weeks

From kickoff to a production AI pipeline for real-time temperature recommendations

Sleepme’s systems now identify the optimal temperature adjustment and mirror it to a customer’s bed within minutes. The feature is a category differentiator for Dock Pro and Cube in a market where most sleep technology doesn’t adapt in real time.

Sleepme’s engineering team reclaimed the hours it would have spent on infrastructure management. Operational overhead dropped. Production workloads and releases got more stable. The team stayed on product innovation — which is where it belongs.

05 What’s Next

Managed AI as the operating model, not the one-time build

The Managed AI engagement continues past the four-week model delivery. As Sleepme adds ML features, the same substrate carries them — no additional infrastructure buildout per feature.