AI / Computer Vision
PrivaCut AI — Privacy-Conscious On-Device Computer Vision Tool
InfraCordeX (Internal Product) · 10 weeks · 2 engineers
The Challenge
Standard cloud-based image processing tools require users to upload confidential documents and personal photos to remote third-party servers, creating data privacy vulnerabilities and compliance risks for individuals and small organizations.
Our Solution
We engineered PrivaCut AI as a privacy-first application where computer vision inference runs entirely on the user’s local device using TensorFlow.js and Capacitor native bridges, ensuring sensitive media never leaves the local environment.
Outcomes
Measurable Results
Zero
Server Media Uploads
All image processing and redaction runs locally on the client device.
Local
Privacy Guarantee
Complete data confidentiality without transmission to external cloud services.
Native
Cross-Platform App
Shared application logic across web and mobile packaging with Capacitor.
Process
Development Approach
Inference Engine Selection
Evaluated on-device machine learning runtimes for client-side execution within mobile web views and browser sandboxes.
Model Optimization & Pipeline
Configured quantized computer vision models for client-side face blurring, background segmentation, and metadata stripping.
Cross-Platform UI
Constructed an intuitive editing interface with Angular, Capacitor camera bridges, and real-time canvas rendering.
Local Storage & Export
Implemented client-side file export pipelines without server roundtrips, guaranteeing zero cloud storage exposure.
Stack
Technology Stack
Angular
Frontend application framework with reactive UI
TensorFlow.js
Client-side computer vision inference engine
Capacitor
Native mobile runtime for camera and filesystem access
FastAPI
Auxiliary backend services for model distribution
Docker
Containerized staging and build pipeline
More Work
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