Machine Learning
for Product Engineers

Protege Engine empowers product engineers to effortlessly embed human-in-the-loop AI capabilities into their products, democratizing access to advanced technology.

Brought to you by Intuitive Systems

Alpha Release

TLDR: Protege Engine

Protege Engine is an AI-driven system developed by Intuitive Systems, designed as a versatile drop-in replacement for Large Language Model Inference APIs like those provided by OpenAI.

Protege Engine TLDR Diagram

It empowers users to create and integrate Large Language Model Inference and training functionality into their products with minimal engineering effort, thanks to its comprehensive RLHF interface and easy-to-use SDK.

By facilitating seamless integration into existing user interfaces, Protege Engine significantly reduces the Total Cost of Ownership (TCO) for AI pipelines. It enhances user outcomes in domain-specific contexts, making it an ideal solution for anyone looking to leverage advanced AI capabilities without the need for expensive and hard-to-find technical resources.

Things you need to know:

  • Predictions: The process involving call and response with prompts and completions from the inference backend, where completions are parsed into labels for further applications, aiding in generating structured outputs.
  • Label Parsers: Tools or mechanisms that take the output (completions) from the inference process and convert them into structured labels, facilitating the interpretation and use of AI-generated data within applications.
  • Feedback Mechanism: A pivotal component where human interaction through the UI leads to the approval or correction of prediction labels, directly influencing the dataset preparation for further training and enhancing model accuracy over time.
  • Inference Backends: The computational backend that performs the AI model's inference tasks. It serves as an instance equipped with an API endpoint for request proxying and execution tracing, crucial for generating predictions.
  • Datasets: Collections of data compiled from various prompts essential for training models. These datasets can be synthetic or standard and are vital for replicating the behavior demonstrated in the prompts, ensuring the model's continuous learning and adaptation.

SDK Release

Protege Engine SDK is a TypeScript library for interacting with the Protege Engine GraphQL API. It's designed for seamless integration in both frontend applications and CLI tooling, providing a robust interface for managing and interacting with LLM training through RLHF (Reinforcement Learning from Human Feedback). The API is built with Prisma and typegraphql-prisma, and the SDK leverages graphql-codegen for typed query inputs and returns.


  • Plug-and-Play AI: Directly integrate with your projects, no specialized AI expertise required.
  • Open Source Advantage: Access the power of open-source models, and existing cloud services like OpenAI — tailor-made for flexibility and innovation.
  • Data Sovereignty: Fully control your data, ensuring privacy and customization to your needs.