Tensorleap is on a mission to make deep learning more understandable, accessible, and most importantly – reliable. By bringing transparency to neural networks, we boost data scientists’ confidence that the models they build will perform reliably in production.
Our platform helps a wide range of industries leverage the potential of deep learning to solve their toughest challenges.



Our Representatives

Monique Lance

VP Marketing, Tensorleap

VP Marketing

Tensorleap

My company offers a first-of-its-kind deep learning debugging and explainability development platform that enables data scientists to understand how their models perceive the data, identi...

Doron Har Noy

Data Scientist, Tensorleap

Data Scientist

Tensorleap

Tensorleap is a Deep Learning Explainability and Debugging platform providing guided error analysis, unit testing, and dataset architecture, enabling data science teams to build high-perf...

Yotam Azriel

CTO & Co-Founder, Tensorleap

CTO & Co-Founder

Tensorleap

Tensorleap is on a mission to make deep learning more understandable, accessible, and, most importantly – reliable. By bringing transparency to neural networks, we boost data scientists’ ...

The Only Debugging and Explainability Development Platform for Neural Networks.

By bringing transparency to deep learning models, Tensorleap eliminates uncertainty and enables data scientists and organizations to dramatically reduce the danger of having their models fail in production.

The Visibility You Need to Build Models You Can Trust

Tensorleap uses innovative interpretability and explainability techniques to revolutionize the neural network development paradigm. By taking the lid off the “black box” of neural networks, Tensorleap also boosts the efficiency of data science teams, cuts costly development cycles, and allows organizations to significantly shorten time to market.

Enjoy Unprecedented Explainability

Even where models perform as expected, data scientists now have the visibility to explain how the model arrived at the result, the ability to discard the possibility of bias, and the confidence to know how their models will stand up in edge cases.

The implications of model failures in production are huge. Understanding how your model perceives the data is mission-critical!