Enterprise AI needs to understand the businesses it serves. Today, that often means supplying models with extensive documentation, schemas and code every time they're asked a question. This approach can be costly, slow and increasingly difficult to scale. Our first research paper, Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models, explores a different approach: embedding enterprise knowledge directly into a model's parameters, rather than repeatedly supplying it through prompts.
In this video, we introduce Internalizer, a lightweight system designed to transfer information from enterprise documents into large language models without retraining the underlying model. We walk through the research, explain how the approach works and share early experimental results demonstrating its potential. The work lays the foundation for a more efficient, adaptable approach to private AI, where organisations can bring their own knowledge to powerful models while retaining control over their data.
Watch the video below to explore the research.