@chandanramola396787093

Manufacturing defects cost the global economy $2.9 trillion annually. Enterprise vision systems cost $40,000–$200,000 — completely out of reach for the 63 million MSMEs in India alone that employ over 110 million people. Nika AI solves this by turning a smartphone into a full-stack AI quality control station. Any factory worker can capture or upload an image and instantly receive a defect analysis powered by a YOLOv8s model trained on 29,354 real industrial images across 7 merged datasets, achieving 83% mAP across 17 defect classes. What makes Nika AI genuinely different is the Hallucination Shield — Monte Carlo Dropout uncertainty quantification running 30 stochastic forward passes, classifying AI confidence as High / Moderate / Low / UNCERTAIN. Instead of giving a wrong answer confidently, Nika AI tells workers when NOT to trust it. On top of detection, Gemma 4 (via Fireworks AI) acts as a domain expert, not a chatbot — delivering structured output covering Root Cause, Severity, Repairability, Prevention Strategy, and Recommended Action for every defect found. The product is complete: JWT-secured multi-role auth (Admin / Supervisor / Worker), full inspection history with filters, an analytics dashboard with defect trends, PDF report downloads per inspection, and deployment on AMD Developer Cloud using ROCm GPU acceleration with $100 hackathon credits. Stack: React 18 + TypeScript (frontend) · FastAPI + Python 3.11 (backend) · YOLOv8s + MC Dropout · Gemma 4 via Fireworks AI · PostgreSQL + SQLAlchemy · ReportLab PDF · Docker + AMD ROCm.

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