Manufacturing defects, security threats, medical anomalies, inventory gaps. Computer vision systems don't get tired, don't lose focus at hour six, and improve with every image they process.
Discuss your computer vision challengeYou have cameras, sensors, and scanners generating millions of images daily. Human inspectors catch 70-80% of issues — good enough for low volume, devastating at scale. Every missed defect is a warranty claim, a safety incident, or a customer complaint. Computer vision closes that gap: consistent, tireless, and measurable.
Defect detection on production lines — surface scratches, dimensional deviations, assembly errors, material inconsistencies. We train models on your specific products using your actual defect samples. Systems run at line speed and integrate with your existing PLC or MES systems to trigger automated rejection or alerts.
Real-time object tracking, crowd analysis, perimeter monitoring, and behaviour detection. We build systems that process live video feeds, detect events of interest, and trigger alerts or actions. Deployed on edge devices for sites without reliable cloud connectivity or where latency matters.
Diagnostic support systems that assist radiologists and pathologists — not replace them. We build AI that highlights regions of interest, measures features, and provides probability scores. All outputs are positioned as decision support, with full audit trails for regulatory compliance.
Specialised OCR, form recognition, handwriting extraction, and image classification. When your input is visual but your output needs to be structured data, we build the bridge. Handles poor quality scans, mixed formats, and multilingual content.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints — even if it means building something nobody's built before.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting. Not by cutting corners — by using AI for the volume work while senior engineers focus on decisions that matter.
Timeline
8-14 weeks, depending on data labelling requirements
Team
2 senior CV engineers + 1 MLOps engineer for deployment
Deliverables
Trained model, deployment package (cloud or edge), labelling pipeline for continuous improvement, monitoring dashboard, integration with existing systems
After launch
Optional retainer for model retraining as new product variants or conditions emerge
An automotive parts manufacturer ran visual quality inspection manually at three points on their production line. Two full-time inspectors per shift caught approximately 73% of surface defects, with a 12% false rejection rate that sent good parts back for unnecessary rework. We deployed camera systems at each inspection point, trained a defect detection model on 15,000 labelled images from their production history, and integrated the system with their existing line control software. Detection rate reached 96% with a 2% false rejection rate. The system paid for itself in four months through reduced warranty claims and eliminated rework.
Representative of a typical engagement. Specific metrics reflect achievable outcomes based on our experience with similar projects.
Most clients don't when they start. We help you build a labelling pipeline — collecting images from your process, setting up labelling tools, and creating annotation guidelines. For initial models, we can work with as few as 500-1,000 labelled images depending on the complexity. The system improves as you collect more data over time.
Both. We deploy models to edge devices (NVIDIA Jetson, Intel NUC, industrial PCs) for sites that need low latency or don't have reliable internet. For multi-site deployments, we use cloud infrastructure with edge inference. We'll recommend the right approach based on your latency requirements, connectivity, and budget.
We design models to be robust to these variations by including them in training data. During deployment, we monitor for distribution shift — when real-world conditions drift from what the model was trained on. When drift is detected, the system flags it and triggers a retraining cycle with new data.
It depends entirely on the use case and image quality. We set accuracy targets with you during scoping and don't declare the project complete until we hit them. Typically, well-scoped defect detection projects achieve 90-97% detection rates. We always provide confusion matrices and per-class performance breakdowns so you know exactly where the model is strong and where human review is still needed.
Tell us what you need to detect, inspect, or monitor. We'll assess feasibility and show you what's achievable with your existing camera setup and data.
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