Project snapshot
- Customer
- Medical Data Analytics Company
- Audience
- Hospital teams and health-tech platforms that need reliable clinical recording in constrained environments
- Pain
- The team needed a standardized mobile solution that could process video and audio locally, without depending on servers for near real-time work.
- Solution
- On-device AI recording app
The challenge
A health-tech platform depended on consistent, high-quality data capture in hospitals. The product needed to record surgical sessions, process signals locally, and turn that data into something the broader platform could trust.
Our approach
Design for the decision, then build the system around it.
We shaped the app around two lightweight model paths: one for audio recognition and one for object segmentation. Both were trained, validated, and converted for Core ML so the workflow could run directly on iOS devices with minimal latency.
Video object segmentation
Core ML deployment for iOS
Solution description
What the recording app was designed to improve.
- Capture surgical video and sound reliably on device
- Detect sounds and objects locally without depending on a server
- Convert the models for efficient iOS deployment
- Create a standardized capture flow for downstream analysis
Illustrative outcomes
What the system is designed to make easier.
- A standardized way to capture clinical video and audio
- Near real-time detection without server-side processing
- A foundation for downstream medical analytics
Technology stack
Built for mobile AI performance.
Models
Mobile
Vision
Results
The recording app captures data reliably at the point of care.
The app creates a standardized way to capture clinical video and audio with near real-time detection on device.
It removed the server dependency from the capture loop and created a cleaner path into the broader analytics platform.
Illustrative note
The case is based on the published project structure and technical brief.
The page emphasizes the mobile AI workflow, on-device processing, and clinical capture constraints from the supplied source content.
