Frequently asked questions

No. Our DART technology is based on few-shot learning: just 10–50 images per class are enough to train a high-precision model. You don't need thousands of samples like traditional systems require.

Typically 2–4 weeks from initial data collection. The proof-of-concept is tested on your real data, so you can evaluate results before committing to full development.

Yes. All our solutions are available both in the cloud (SaaS) and on-premise. The on-premise architecture is ideal for organizations with data sovereignty requirements, air-gapped networks, or strict regulatory constraints.

All solutions expose documented REST APIs. Integration with ERP, MES, CRM, or custom systems happens via standard endpoints. We provide SDKs and technical support during integration.

Our DART engine achieves 99.9% accuracy with zero false positives, even on catalogs with tens of thousands of classes. Performance is validated on your specific use case during the prototype phase.

Yes. Thanks to metric learning, adding a new class only requires a few reference images — no retraining needed. The system is open-set: it recognizes objects never seen during training.

We offer guaranteed SLAs with continuous performance monitoring, periodic model updates, and a dedicated support team. We intervene proactively if KPIs drop below threshold.

We work across sectors: industrial manufacturing, retail, logistics, collectibles, and corporate knowledge management. Our technology is domain-agnostic in its application.