‍

VisionQC Active model training:
~6,000–12,000 GPU/accelerator
hours (Apple Silicon + hybrid compute)
‍Inference + self-learning cycles:
Tens of thousands of continuous hours
(scanners, schedulers, watchdogs, dashboards)Always-on systems:
VisionQC, AudioQC, Theater Mode
scanners run 24/7

5 Engines inside one Autonmous system.
Visual Intelligence
& Quality Control
Sound Integrity
& Acoustic Intelligence
Subtitles, Metadata,
Semantic Accuracy
Visual Consistency, Grade,
Signal Fidelity
Reconstruction, Reformatting
& Cinematic Intelligence

Quality Control AutomationSavings:
20%–60% of QC labor costs
‍Automated visual, audio, and data inspectionFewer human review
passes Continuous monitoring instead of spot checks
‍Example:
A facility spending $2M/year on QC labor
→ $400K–$1.2M saved annually