Programming AI can be complicated, but it doesn't have to be

From Augmentation To Autonomy

Across the vast spectrum of AI use cases Bonsai is focused on simplifying the programming of control and optimization so developers can build more intelligent systems and business processes

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BRAIN Building

The Bonsai Platform empowers a developer to build, teach and use intelligence models


An AI learns from interacting with a simulation or analyzing recorded data. With Bonsai each AI is created by following the three steps outlined below.

Step 1: Build
  • Create a BRAIN - a high level model of the concepts to be learned and a set of lessons that can be used to teach them - using Bonsai’s Inkling programming language.
  • Load the resulting project (the collection of your Inkling code, data, and simulations) into the Bonsai AI Engine using Bonsai’s CLI, IDE, or web based tooling.
  • Specify any pertinent training sources, such as data or simulations that will be used in conjunction with the lessons as part of teaching the model.
  • Filter data, configure simulations, or otherwise prepare the training materials as appropriate for each lesson.
  • Establish objectives used to evaluate the AI’s mastery of each concept. This is typically a scoring function assessing the quality of the AI’s prediction versus desired results.
Step 2: Teach
  • Start the training of your BRAIN in the Bonsai AI Engine - this will generate an appropriate low level model for your project (e.g.  a deep learning neural network topology).
  • Assess training status throughout the training of your BRAIN.
  • Refine and iterate your project as desired, rerun training of the BRAIN as needed.
Step 3: Use
  • Connect your BRAIN via Bonsai provided libraries to your software or hardware application (just like you would connect a database to your application).
  • Your application will be able to stream in data and receive predictions from your BRAIN.
  • Your Inkling code can be leveraged in other applications.
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