InfraCordeX 
01 · Executive Overview
PrivaCut AI is a privacy-first mobile engineering solution built to process sensitive biometric identity photos entirely within client-side sandboxes. By shifting computer vision segmentation workloads from centralized GPU cloud clusters onto on-device WebGL and WebAssembly runtimes, the system eliminates recurring cloud compute costs while ensuring Typed request/response contracts data sovereignty for end-users.
02 · The Problem & Engineering Constraints
Conventional biometric photo utilities rely on server-side processing pipelines where raw portrait imagery is transmitted over REST endpoints to cloud GPU instances. This architecture introduces severe data privacy liabilities (GDPR and biometric regulation exposure), recurring infrastructure compute costs per transformation, and latency bottlenecks when users operate on bandwidth-constrained mobile networks.
03 · Measurable Engineering Impact
No server-side image processing
Server Compute Latency
Local On-Device Compute
Typed request/response contracts
Data Sovereignty
Biometric Data Stays On Device
Hardware-accelerated rendering
Viewfinder Frame Rate
Smooth Overlay on Mid-Tier Hardware
04 · Technical Solution & Strategy
Architected an offline-first computer vision pipeline using localized WebGL and WebAssembly segmentation models running within a Capacitor native bridge. Designed reactive Angular Signals state graphs that coordinate camera viewfinder streams, contour detection, automated aspect ratio calibration, and multi-country print formatting directly on the user mobile silicon.
Edge Computer Vision Pipeline
Local WebGL/WebAssembly segmentation network executing client-side contour extraction without server roundtrips.
Angular Signals State Flow
Fine-grained reactive state graph synchronizing real-time camera feedback loops with smooth UI performance.
Zero Cloud Compute Overhead
Shifting transformation logic to client silicon eliminates recurring server GPU operational costs entirely.
05 · Role & Core Contributions
Engineered the client-side architecture using Angular 19+ (Zoneless, Signals) and Ionic/Capacitor native runtime.
Implemented WebGL canvas compositing pipelines for real-time background subtraction and sub-pixel edge smoothing.
Built a lightweight PHP 8.4 REST service for dynamic specification schemas and regulatory dimension profile delivery.
Configured automated Android build pipelines and Google Play Console release workflows.
06 · System Architecture & Subsystems
The system is decoupled into an on-device edge processing client and a lightweight metadata microservice. The client executes WebAssembly/WebGL models in a sandboxed thread, streaming image transformations directly to the HTML5 Canvas context. Angular Signals manage viewfinder states and calibration masks with Angular Signals architecture.
Edge Inference & Compositing Engine
On-Device Computer VisionLocal WebGL/WebAssembly segmentation network executing client-side contour detection and alpha-matte composition without external network calls.
Reactive Client Application
Mobile UI & Viewfinder ControlZoneless Angular application running on Ionic and Capacitor providing a 60FPS viewfinder overlay and print grid generator.
Regulatory Schema Microservice
Metadata & Specification BackendStateless PHP 8.4 REST API providing updated ICAO and ISO/IEC 19794-5 passport dimension metadata to client devices.
07 · Key Technical Decisions & Tradeoffs
Client-Side WebGL Inference over Server-Side GPU Endpoints
Eliminates recurring per-request cloud GPU infrastructure costs and provides complete biometric privacy compliance.
Segmentation model payload size had to be aggressively quantized under 8MB to prevent mobile memory pressure.
Angular Signals State Graph over Monolithic Store Libraries
Removes heavy store boilerplate and eliminates Zone.js change-detection overhead during 60FPS camera stream overlay updates.
Required explicit computed signal composition for manual mask correction undo/redo state stacks.
Stateless Microservice for Dimension Specifications
Decouples regulatory photo rule updates from binary app store releases, allowing remote compliance updates.
Clients must cache dimension definitions locally to maintain full offline usability when network is absent.
08 · Deployment & Production Infrastructure
Client application compiled into optimized Android App Bundles (AAB) via Android SDK / Gradle and published to the Google Play Store. The specification metadata API is deployed on a Linux VPS behind Nginx with TLS 1.3 encryption and Cloudflare CDN caching.