Automate your quality assurance with enterprise-grade computer vision. Detect micro-defects in real-time with 99.9% accuracy.
Streamline your quality control process with our intuitive three-step workflow.
Use your device camera or upload high-resolution images of your components directly to our secure platform.
Our advanced computer vision models instantly analyze the media to detect micro-defects, cracks, and anomalies.
Receive immediate, detailed reports with defect classification and confidence scores to make quick decisions.
A complete suite of AI-powered tools designed to transform your inspection workflow.
Powered by state-of-the-art computer vision models
YOLO-based defect detection with enterprise precision
Instant analysis with sub-second response times
Deploy on-premise or cloud with IoT integration
ML-powered failure prediction and trend analysis
Comprehensive support for standard testing protocols
Automated surface crack and corrosion detection
Particle accumulation pattern analysis
AI-enhanced signal processing for internal flaws
Deep learning analysis of X-ray imagery
Built for scale, security, and compliance
Comprehensive insights and performance metrics
Bank-grade encryption and compliance standards
ISO 9001, ASTM compliant quality assurance
Tailored detection parameters for your materials
Tailored solutions for specific manufacturing challenges across diverse sectors.
Automated inspection of weld seams, surface finish, and assembly verification.
High-speed verification of PCB components, solder joints, and connector alignment.
Ensure label integrity, seal quality, and code legibility at production speeds.
Detect structural defects in large-scale metal components and infrastructure.
See how leading manufacturers are achieving zero-defect production with Chitti AI.
Reduction in inspection time
"Chitti AI has completely transformed our weld inspection process. What used to take minutes now takes seconds."
Annual savings per line
"The accuracy of defect detection has helped us eliminate false positives and reduce material waste significantly."
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