WorldsNQ provides a centralized, searchable index for assembling and managing all your training data for computer vision applications. Users can create and fine-tune custom CV models at 100x the speed of typical computer vision training with options to auto-label or augment using generative AI.

WorldsNQ automatically identifies all objects in your training data where users leverage a search engine unique to Worlds to identify and amplify similar objects based on size, shape, color, or other attributes to quickly label all matches across the dataset. Simultaneously, WorldsNQ suppresses background information irrelevant to the training, further improving model quality and performance. Since all models are created exclusively from your data, there is no chance of model contamination or IP leakage.

The combination of these features enables you to create a comprehensive global data library of your physical world to power your real-world AI applications with unparalleled speed and quality.

Speed: 99% reduction in human effort associated with annotation. Reducing delivery timeframes and labor error rates.

Data Quality: Ensuring you’ve captured everything in your dataset is critical. Without a searchable index of your video, unseen or unanticipated objects can disrupt your model. WorldsNQ provides a validation layer to verify data completeness, addressing common blind spots in machine learning.

Optimized Training Lifecycle: Training for enterprise AI solutions is an ongoing process due to real-world changes. Environmental changes like weather or new equipment can quickly affect model performance. Maintaining an updated knowledge base and continuously accommodating new anomalies is vital. This ensures models reflect real-world dynamics and improves overall accuracy and effectiveness.

Data Sources & Model Sizing: Client specific data is unmatched in training effectiveness. Ensuring data variety and proper management across different cameras is essential. WorldsNQ supports training across data sources and optimizing models based on hardware constraints, ensuring high performance regardless of data volume or diversity.

Privacy Centric: AI models based exclusively on company source imagery deployed in company operated cloud or dedicated infrastructure.

Multi-Model Support: Different models handle different tasks, but they all rely on well-managed data. Separating data, quality, and management from training and model architecture is crucial for integrated solutions.

To learn more about the addition of the comprehensive WorldsRT platform for a seamless deployment process, visit worlds.io/platform/worldsrt