Train AI Like An Intern Not A Pet

Business User Led Post-training at Scale

Enterprise AI suffers from a fundamental limitation: even the best models cannot know the unique terminology, processes, and context of your business. Aible solves this by enabling business users to train AI agents directly through natural language feedback on their reasoning. Instead of training AI like a pet with rewards and punishments, Aible trains AI Agents Like Interns Not Pets where users explain what step was wrong and how to fix it. This makes feedback immediately actionable, allowing models to improve dramatically. With just hundreds of examples, small models can match or exceed models 100x larger, continuously and securely improving through automated post-training.

The feedback process is completely automated, requiring no data science skills, and is provided by end-users. Importantly, business users are incentivized to provide feedback because they get immediate payback - improved answers to their questions.

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Aible Intern Model

Aible’s initial testing was revealed at NVIDIA GTC 2025, where a 3 Billion parameter model performed as well as a 100X larger OpenAI o3 model with just 1000 improvement examples. This indicates that adding reasoning to a model can significantly improve the accuracy of the model. It also opens up the tantalizing possibility that end-users can provide feedback on reasoning steps of the model instead of just the output. Traditionally, we have trained AI like we train a pet (relying on simple rewards or punishments based on its output) but now we can start training it like an intern (providing it feedback on the way it reasoned and offering it guidance on the tools it can use to get better results, such as what enterprise API it can invoke to access necessary information).

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Industrialized Agents at Scale

At GTC 2025, Aible highlighted three key trends that are converging in the world of AI

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CUSTOMER VIDEO

Train your AI Agents Like Interns, Not Pets

Instead of training AI like a pet with rewards & punishments, you get greater performance by training it like an intern with end-user feedback on its reasoning steps.

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