Computer Vision Solutions
Teaching machines to see what matters in your visual data.
We build production computer vision systems — object detection, image classification, OCR, video analytics, and on-device inference — that automate visual inspection, power intelligent apps, and extract structured data from unstructured images at scale.
Visual Intelligence for Real Business Problems
Computer vision has crossed the threshold from research curiosity to reliable production technology. Modern CV models can identify defects on a production line, extract structured data from scanned invoices, count inventory from CCTV footage, read licence plates, verify identities, and analyse medical imagery — at accuracy levels that match or exceed human performance in many domains.
CodeWingz builds computer vision systems that solve specific, measurable business problems — not technology demonstrations. We begin with your use case and work backward to the right model architecture, deployment environment, and accuracy target. That might mean a YOLOv8 model running on-device in a Flutter app, a cloud-based OCR pipeline processing 10,000 documents per day, or a real-time video analytics system on edge hardware.
Every CV system we build is evaluated on production-representative data, not held-out benchmark sets. We care about the accuracy your users experience, in your lighting conditions, with your image quality, on your devices.
Service Inclusions
Object Detection & Tracking
YOLOv8, DETR, and custom detection models for identifying, localising, and tracking objects in images and video streams in real time.
Image Classification
Multi-class and multi-label classification models for product categorisation, defect detection, medical image triage, and content moderation at scale.
OCR & Document Intelligence
Structured data extraction from invoices, receipts, ID documents, and forms using PaddleOCR, Tesseract, or Azure Form Recognizer with post-processing validation.
On-Device Vision (Mobile)
TensorFlow Lite and Core ML models deployed inside Flutter and native iOS/Android apps for offline, zero-latency, privacy-preserving visual intelligence.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Codewingz follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Use Case Definition
We precisely define the visual task, required accuracy, latency constraints, deployment environment, and data collection requirements.
Data Collection & Annotation
We help design your data collection process, set up annotation tooling (Roboflow / CVAT), and — if needed — can source annotation from our vetted labelling pipeline.
Model Selection & Baseline
We evaluate 2–3 candidate architectures on your data, establish accuracy baselines, and select the approach with the best accuracy/latency/size trade-off.
Training & Optimisation
Full training run with hyperparameter optimisation, data augmentation, and class-balance tuning. Confusion matrix and per-class accuracy analysis.
Deployment & Integration
Model exported to target format (ONNX, TFLite, CoreML, TensorRT), deployed to cloud API or embedded in mobile app, integrated with your data pipeline.
Production Monitoring
Confidence score distribution monitoring, accuracy tracking on flagged edge cases, and periodic model updates as new data accumulates.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
YOLOv8
Ultra-fast object detection.
PyTorch
Deep learning framework.
TensorFlow Lite
Deploying ML on mobile and edge devices.
Roboflow
Dataset management and computer vision pipelines.
OpenCV
Open source computer vision library.
NVIDIA TensorRT
High-performance deep learning inference.
Real-World Impact
PackSmart Manufacturing
The Challenge
“A food packaging plant was performing visual quality inspection manually — 3 operators on rotating shifts checking packaging for label misalignment, seal defects, and contamination. Human fatigue caused defect escape rates to spike on night shifts, and the manual process was a bottleneck limiting line speed.”
The Solution
We deployed a YOLOv8-based defect detection system on 4 production line cameras, processing 30 frames per second per camera. The system detects 6 defect classes with a 99.1% accuracy rate, triggers an automatic line stop for critical defects, and logs all detections with image evidence for QA reporting. Edge deployment on NVIDIA Jetson hardware achieves 18ms inference latency with no cloud dependency.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
What Should Your Cameras Be Telling You?
Share a sample of your images or describe the visual task — we will assess feasibility and define the accuracy you can realistically achieve.