Deep Learning Services that make your systems see, understand, and act in real time.

The amount of data required depends on your use case, image quality, and model complexity. Most computer vision projects using pre-trained models typically require between 500 and 5,000 labelled images per category for reliable performance. More complex environments or rare defect scenarios may require additional data.

We begin every project with a data assessment to evaluate your existing datasets and identify gaps. To reduce manual labelling effort, we also use data augmentation, synthetic data generation, and active learning techniques to improve model accuracy with less data.

In many cases, yes. Most modern IP cameras and CCTV systems can be integrated into AI-powered vision solutions without replacing your existing infrastructure. We assess your cameras, network setup, and edge hardware during the discovery phase to determine compatibility and performance requirements.

Our solutions support Intel, NVIDIA, ARM-based edge devices, and standard x86 systems. If upgrades are required, we recommend the minimum hardware needed to achieve reliable real-time performance while staying within your budget.

A focused computer vision deployment such as defect detection, safety monitoring, or theft detection for a single site  typically takes between 6 and 12 weeks from scoping to production deployment.

This usually includes data assessment, model training, testing, edge deployment, integration, and validation. Larger multi-site or multi-camera systems may take several months depending on scale and operational complexity. We provide a detailed roadmap, milestones, and deployment plan before development begins.

Yes, maintaining long-term model accuracy is a critical part of production AI deployment. Visual conditions naturally change over time due to lighting variations, new product types, seasonal differences, or operational changes.

To address this, we build automated monitoring, drift detection, and retraining workflows into every deployment. The system continuously tracks performance metrics and alerts teams when accuracy drops below defined thresholds. This ensures the model stays reliable and production-ready over time.

Privacy and security are built into the system architecture from the beginning. In most deployments, AI inference runs directly on local edge hardware, meaning video footage does not leave your facility or require cloud processing.

Where cloud infrastructure is needed, we implement encryption, access controls, anonymisation, and compliance-focused data handling practices. We also support GDPR, CCPA, HIPAA, and industry-specific security requirements depending on your operational environment.

Yes. We build secure API layers that expose model outputs to your existing business systems ERP, MES, WMS, SCADA, or any system with an API or database. Real-time inference results, alerts, and confidence scores can be pushed to your dashboards, workflows, or ticketing systems without requiring any changes to your existing architecture. We handle authentication, rate limiting, and error recovery across every integration point.

Privacy is addressed at the architecture level, not as an afterthought. For most deployments, inference runs entirely on-premise on edge hardware raw video footage never leaves your facility or reaches the cloud. Where cloud processing is required, we implement end-to-end encryption, data minimisation, and anonymisation (blurring faces and identifiable information before any data leaves the edge). We assess against GDPR, CCPA, and sector-specific regulations and provide a documented privacy impact assessment before go-live.