Optimizing an AI pipeline to interpret 200+ ASL/BSL signs concurrently under 80ms

Hey devs,

Working on the engineering architecture for Uvilox AI (uvilox-aiwebsite.pages.dev). It's a real-time sign language to voice translator for emergency calling and digital medical care.

To make it viable for high-stakes calls, standard web APIs and heavy model inference weren't cutting it. We had to move away from full-frame rendering and build a custom concurrent pipeline that maps body language vector spaces, hand coordinates, and facial expressions simultaneously.

Currently hitting a 97.4% accuracy rate. I’d love to talk shop with anyone specializing in real-time computer vision optimization, low-latency WebRTC pipelines, or building HIPAA-compliant secure data streams. Drop your repo or stack thoughts below!