What types of computer vision problems can you solve?

We build custom CV systems for object detection and classification, quality control and defect inspection, anomaly detection, real-time monitoring, document intelligence (OCR and form extraction), predictive maintenance through visual inspection, and safety compliance monitoring. If your problem involves analyzing visual data—images or video—we can likely help.

How accurate are computer vision systems in real-world industrial conditions?

Production accuracy depends on your specific use case, data quality, and operating conditions. Our deployed systems typically achieve 94-99% accuracy for classification tasks and 85-95% for complex detection scenarios. We design for real-world challenges like poor lighting, occlusion, camera angle variations, and environmental factors that break demo systems. Every project includes accuracy benchmarking against your actual conditions, not lab datasets.

What data do you need to train a custom computer vision model?

Data requirements vary by problem complexity. Simple binary classification tasks may need a few thousand labeled images, while multi-class object detection with rare edge cases requires significantly more. The key is quality over quantity—images must represent the actual scenarios your system will encounter, including edge cases and failure modes. We can work with your existing image datasets, help you design data collection protocols, or use synthetic data augmentation to reduce labeling effort. Our technical assessment defines exact data requirements for your use case.

Can computer vision work on edge devices or does it need cloud infrastructure?

We deploy CV systems on edge devices (NVIDIA Jetson, Coral TPU, Intel Movidius, Raspberry Pi), cloud infrastructure (AWS, Azure, GCP), or hybrid architectures depending on your latency, privacy, and connectivity requirements. Edge deployment is ideal for real-time monitoring with minimal latency, offline operation, and data privacy. Cloud deployment works well for batch processing, centralized analytics, and systems that don’t need sub-second response times.

How long does it take to develop and deploy a custom computer vision system?

Development timeline depends on problem complexity, data availability, integration requirements, and your internal review processes. Our technical assessment provides a detailed project roadmap with realistic milestones specific to your requirements.

What’s the difference between using off-the-shelf computer vision tools vs. custom development?

Off-the-shelf tools (cloud APIs, SaaS platforms) work well for common use cases with standard datasets—think facial recognition, general object detection, or basic OCR. Custom development is necessary when: your use case is specialized (detecting specific defects, rare objects, industry-specific scenarios), you need to operate in challenging conditions (poor lighting, extreme angles, unusual environments), you have strict latency or privacy requirements, or existing tools don’t achieve the accuracy your business demands. We assess both options during our technical evaluation and recommend the most cost-effective path.

How do you ensure computer vision systems work reliably over time as conditions change?

We implement active monitoring to track model performance, detect accuracy drift, and flag when retraining is needed. Every deployed system includes performance dashboards, automated alerts for degraded accuracy, data collection pipelines to capture edge cases, and regular model health reviews. When conditions change—new products, lighting upgrades, camera repositioning—we provide model updates and retraining services to maintain accuracy.

How much does a custom computer vision project cost?

Project cost depends on scope, technical complexity, data requirements, integration needs, and deployment infrastructure. Our technical assessment provides a fixed-price proposal tailored to your specific use case. We work with organizations of all sizes, from targeted pilot projects to enterprise-scale deployments.

What’s included in your technical assessment?

Our assessment includes: problem definition and feasibility analysis, data requirements review (volume, quality, labeling needs), technical architecture design (model selection, infrastructure, deployment strategy), accuracy and performance benchmarks, integration and deployment planning, risk analysis and mitigation strategies, and a detailed proposal with fixed-price quote. The assessment gives you a complete roadmap before committing to development.

Do you provide ongoing support and maintenance after deployment?

Yes. We offer managed support agreements that include: system monitoring and alerts, regular model performance reviews, retraining and updates as needed, integration support for new cameras or data sources, security patches and infrastructure updates, and technical support with guaranteed response times. Support packages are tailored to your uptime and accuracy requirements.

Can you integrate with our existing systems (cameras, databases, workflows)?

Absolutely. We specialize in integrating CV systems into existing infrastructure. We work with standard protocols (RTSP, MQTT, WebRTC, REST APIs), connect to most IP cameras and video management systems, integrate with databases (SQL, NoSQL, data warehouses), push results to your business systems (ERP, CRM, SCADA), and deploy on your preferred infrastructure (cloud, on-premise, edge). Integration planning is part of every technical assessment.

How do you handle data privacy and security?

We implement security at every layer: data encryption in transit (TLS) and at rest (AES-256), role-based access controls and audit logging, compliance with GDPR, HIPAA, or industry-specific regulations as needed, option to deploy entirely on-premise or in your private cloud (no data leaves your infrastructure), and regular security audits and penetration testing. For sensitive applications, we can train models using federated learning or differential privacy techniques.

What happens if the computer vision system doesn’t meet accuracy targets?

We establish clear accuracy benchmarks during the assessment phase and validate them with POC testing before full development. Our contracts include performance guarantees tied to agreed accuracy metrics. If deployed systems don’t meet targets, we provide additional model tuning, data collection, and retraining at no extra cost until benchmarks are achieved. This is why we emphasize rigorous POC validation—we don’t move to production until accuracy is proven.