The Challenge
Manual cashier input in high-volume F&B environments leads to frequent errors, customer complaints, and bottlenecked queues. The core issue is product identification—cashiers struggle to quickly recognize diverse menu items on a mixed tray, leading to lost revenue and frustration.
- High error rate in manual billing.
- Long queues during lunch and dinner rush.
- Revenue leakage from unbilled items.
The AIQU Systems Solution
We deployed an Overhead Computer Vision System trained on the restaurant's specific menu. It instantly segments and classifies every item on a tray—distinguishing between similar dishes—and pushes the final bill directly to the POS API in milliseconds.
- Instance segmentation for overlapping items.
- Sub-second inference time at the edge.
- Seamless POS integration (No manual entry).
System Architecture
Camera Feed
Overhead 4K Stream
Vision Pipeline
- Tray Detection
- Item Classification
- Confidence Scoring
Edge Compute
NVIDIA Jetson / Local
POS API
Bill Generation
Technology Stack
Vision Models
YOLOv8, Detectron2
Training Data
Custom Labeled Dataset (CVAT)
Hardware
Edge AI (Jetson Orin)
Integration
REST API / WebSocket
Business Impact
3x
Faster Queue Throughput
0%
Billing Errors
Higher GMV / Hour
Customer Experience
Delivery Roadmap
Phase 1
Data Collection
2-4 Weeks
Phase 2
Model Training
4-6 Weeks
Phase 3
POS Integration
4 Weeks
Phase 4
Pilot & Scale
Deployment