Today, we are announcing the v1 launch of the Nuron.World API. NuronLabs was founded with the goal of enabling truly safe & reliable real-world neural networks in production applications and this milestone brings us one step closer to achieving that goal. A well designed API is essential because it allows for deeper integration into the production application stack. This unlocks the ability to learn from and adapt to data automatically resulting in greatly compounded value over time. Most importantly, this allows real-time safety monitoring to verify that the neural networks are performing reliably.

What you can do with the Nuron.World API

There are many components to ensuring safe and reliable deep learning in the real world from the data used to train the model to monitoring real-time changes in the environment. Our infrastructure provides a vertically integrated solution that jointly optimizes various components and detects potential issues before they crop up to prevent unintended side effects during deployment.

Location-specific learning

  • Build your own custom lattice of location-specific networks over a specified geography.
  • Learn location-specific behaviors over time (e.g. entities, driving patterns, weather conditions).
  • Manage and deploy location-specific models in your production application.

Safety & reliability

  • Automatically determine geofences that maximize safety & reliability for your use case.
  • Monitor safety & reliability (anomaly detection, distribution-shift tracking and more) at any location in real-time.


  • Grow your dataset by orders of magnitude by searching for richly labelled images that match a given location’s distribution.
  • Access 2M richly annotated driving and sidewalk images in San Francisco or import your own dataset.

A huge thanks to all our beta-testers who have been essential in refining the platform. We have a few extremely powerful features in the pipeline so stay tuned.

Sign up here to get access to the API!

Building a neural layer for reliable real-world deep learning