Real-Time AI Quality Inspection
Hybrid CNN-ViT + vLLM Vision System with DetectClaw Agentic Execution
The Problem We Solve
Manual inspection is costly, inconsistent, and blind to data
High Labor Cost
Mid-sized factories spend 15-20% of revenue on manual QC
Inconsistent Quality
20-30% error rate damages customer trust and product reputation
No Data Insights
Manual data entry blocks process optimization and issue traceability
Our Product
Three-Layer AI Architecture
Hybrid AI Vision System
CNN extracts local features, ViT models global context, vLLM reasons about defects. The BRAIN of the system.
Learn More →Agentic Execution Layer
DetectClaw agents communicate directly with upstream machinery, adjusting pressure, temperature and parameters in real-time. The NERVE system.
Learn More →Autonomous Workflow
Auto-trigger ERP updates, PLC rejection, parameter adjustments, and alert notifications. The BODY of operations.
Learn More →System Architecture
From image acquisition to autonomous execution
Technical Advantages
Three moats that set us apart
Self-Correcting System
DetectClaw auto-starts federated learning when confidence drops on edge cases
Instant Adaptation
DetectClaw reads new CAD specs and adapts inspection criteria instantly
Edge-Cloud Synergy
Real-time inference on NVIDIA Jetson with cloud-based model training
Application Scenarios
Deployed across the entire quality workflow
Inline Inspection
Real-time defect detection and automatic sorting on production lines
Incoming QC
Quality checks on raw materials and components from suppliers
Finished Product QC
Final quality inspection before products leave the factory
Customer Cases
Real results from real deployments
12% Yield Improvement
8x faster inspection, 0.3% miss rate, HK$350K monthly savings
Request case studyFully Automated QC
DetectClaw integrated with PLC, defect trace in 10 min vs 3 days
Request case studyZero-Swap Setup
CIR adapts in 3 min, line changeover reduced 90%
Request case studyOur Product
Three-Layer AI Architecture
Hybrid AI Vision System
CNN extracts local features, ViT models global context, vLLM reasons about defects. The BRAIN of the system.
- CNN: Convolutional layers for local feature extraction
- ViT: Vision Transformer for global context modeling
- vLLM: Large language model for defect reasoning
Agentic Execution Layer
DetectClaw agents communicate directly with upstream machinery, adjusting parameters in real-time. The NERVE system.
- Direct communication with upstream PLC/machinery
- Real-time parameter adjustment (pressure, temperature)
- All decisions driven by DetectClaw execution layer
Autonomous Workflow
Auto-trigger ERP updates, PLC rejection, parameter adjustments, and alert notifications via DingTalk. The BODY of operations.
- ERP inventory auto-update
- PLC automatic rejection of defective products
- Real-time alerts via DingTalk and email
System Architecture
From image acquisition to autonomous execution
Industry Solutions
Precision defect detection across manufacturing sectors
Electronics
High-precision detection of semiconductor, PCB & chip defects
- Microscopic defects on PCB traces and pads
- Chip surface contamination and scratches
- Solder joint and BGA package inspection
Automotive
Scratch, defect & consistency inspection on complex surfaces
- Scratches on painted and chrome surfaces
- Weld joint defect detection
- Assembly consistency verification
Metal Processing
Surface inspection for scratches, pits & rust on metal
- Surface scratches and pits on metal sheets
- Rust and oxidation detection
- Coating uniformity inspection
About Us
Pioneering the future of industrial quality inspection
Our Mission
Eliminate manual quality inspection with AI-powered autonomous systems
R&D Excellence
Validated by Prof. Chen Ma (CityU), published in ICCV, AAAI, IEEE
Core Technology
Hybrid CNN-ViT-vLLM architecture with Shapley XAI and CIR retrieval
R&D Roadmap
"Our mission is to eliminate manual quality inspection by building AI-powered autonomous systems that see, reason, and act at machine speed."
Customer Cases
Real results from real deployments
12% Yield Improvement
8x faster inspection, miss rate reduced to 0.3%
Request case studyFully Automated QC
DetectClaw integrated with PLC, defect trace in 10 min
Request case studyZero-Swap Setup
CIR adapts in 3 min, line changeover reduced 90%
Request case studyResources
Whitepapers, case studies, and technical documentation
Technical Whitepaper
Deep dive into Hybrid CNN-ViT-vLLM architecture and benchmarks
Case Studies
Detailed deployment stories across electronics, automotive, and metal
API Documentation
Integrate DetectClaw into your existing production line systems
Blog & Updates
Latest technical insights, product updates, and perspectives
Contact Us
Ready to transform your quality inspection?