Computer Vision Development
Manual inspection does not scale.
Your production line cannot wait.
We build computer vision systems that detect defects, track objects, and analyse video in real time — on your factory floor, at your inspection stations, or at the edge without cloud dependency. Custom models trained on your data, not generic benchmarks.
Discuss Your Vision Project →Custom Models for Your Visual Domain
Generic computer vision models fail in production environments because your defects, lighting conditions, camera angles, and product variations are unique. We build and train models specifically on your data — not pre-trained ImageNet weights fine-tuned for an hour.
Our process starts with a feasibility assessment: we evaluate your imaging setup, representative samples of defect and non-defect cases, and production throughput requirements. We set honest accuracy targets before committing to deployment.
We handle the full pipeline: dataset collection and annotation, model architecture selection, training and evaluation, optimisation for your target hardware, and production deployment with monitoring. Edge deployment on NVIDIA Jetson hardware is a common requirement for our manufacturing clients.
Beyond manufacturing inspection, we build vision systems for retail (shelf monitoring, customer analytics), logistics (parcel sorting, damage detection), and document processing (OCR, form digitisation, identity verification).
What We Build
End-to-end vision systems from data annotation to edge deployment.
Quality Inspection & Defect Detection
Automated visual inspection for manufacturing lines — detecting surface defects, dimensional anomalies, assembly errors, and packaging issues at production speed.
Object Detection & Tracking
Real-time detection and tracking of objects, people, vehicles, and custom entities in images and video streams. Custom YOLO and transformer-based models trained on your data.
Video Analytics
Activity recognition, crowd analysis, occupancy monitoring, and safety compliance detection from CCTV and IP camera feeds. Edge or cloud deployment based on latency requirements.
Document & Text Recognition (OCR)
Extraction of text from images, scanned documents, product labels, and handwritten forms. Layout-aware processing for tables, forms, and structured documents.
Edge AI Deployment
Model optimisation and deployment on NVIDIA Jetson, Raspberry Pi, and custom industrial edge hardware for on-device inference without cloud dependency.
Custom Dataset & Annotation
Annotation pipeline setup, quality assurance, and augmentation strategies for building training datasets. Active learning workflows to minimise labelling cost.
Technologies We Work With
State-of-the-art vision stack, optimised for your hardware and latency requirements.
Common Questions
What types of defects can computer vision detect in manufacturing?
Surface defects (scratches, cracks, dents, discolouration), dimensional anomalies (out-of-spec measurements), assembly errors (missing components, misalignment), and packaging defects (label errors, seal failures). Detection performance depends heavily on defect visibility, image quality, and lighting consistency. We conduct a feasibility assessment before committing to accuracy targets.
How much training data do we need for a computer vision model?
It depends on the complexity of the task and the variability of your data. For defect detection on a single product type with consistent lighting, 500–2,000 annotated images per class is often sufficient with transfer learning. For highly variable scenes or rare defect types, you may need 5,000–20,000+ images. We design active learning pipelines to minimise annotation cost by prioritising the most informative samples.
Can computer vision run on-premise without cloud connectivity?
Yes. We regularly deploy vision models on NVIDIA Jetson devices, industrial PCs, and custom edge hardware for applications where real-time processing is required, network connectivity is unreliable, or data must not leave the facility. Edge models are optimised using TensorRT for maximum inference speed on available hardware.
What inference speed can we expect for real-time production line inspection?
YOLOv8 on an NVIDIA Jetson AGX Orin achieves 60–120 FPS for standard detection tasks, depending on image resolution and model size. On an NVIDIA RTX A4000, speeds exceed 200 FPS at 1080p. We size the hardware and model architecture to your required throughput and latency budget during the design phase.
How do you handle model retraining as new defect types appear?
We build continuous learning pipelines that flag low-confidence predictions for human review, accumulate new labelled samples, and trigger automated retraining when dataset thresholds are met. The model improves over time as it encounters new edge cases, and you maintain a full audit trail of model versions and performance metrics.
Ready to automate visual inspection?
Share your imaging setup and defect types with us. We will assess feasibility and accuracy targets before you commit to a project.
Start the Conversation →
