TensorLeap

Tensorleap is a debugging and explainability platform for deep learning models, positioned for physical AI such as robotics, autonomous vehicles, semiconductors, agritech, healthcare and defense. It analyzes how a neural network interprets data and why it fails, linking activations, data and semantics so teams can inspect behavior at the concept level, from the lab through live production. The platform covers four areas. Model behavior analysis detects failure modes, edge cases and domain gaps and traces them to a root cause. Dataset curation guides labeling priorities, removes redundant data and suggests where synthetic data would help. Model optimization offers guidance on loss functions, hyperparameters, retraining subsets and architecture. Production monitoring detects drift and regressions in live systems. The vendor describes this as a closed loop of diagnosing, applying a fix, re testing and monitoring in one workspace. It is built for teams that train their own networks and works with any deep learning model. It is described as integrating with PyTorch, TensorFlow, Weights and Biases, MLflow, and S3, GCS and Azure Blob storage. It can run in the customer's cloud or on premise, with single sign on, role based access control and audit logs. The vendor presents a case study with Hexagon reporting a 40 percent dataset reduction. Pricing is not published, and access starts with a demo booking.