Foundation Model
A large model pre-trained on broad data at scale that can be adapted to many downstream tasks - the general-purpose base layer of modern AI.
A foundation model is a single model trained on a very large, general corpus so that it can be reused across many tasks rather than built for one. Large language models are the best-known example, but the term also covers image, audio, and multimodal models. Their defining trait is generality: one base model, adapted by prompting, retrieval, or fine-tuning to countless applications.
Foundation models concentrate capability - and risk - in a shared dependency. Many teams build on the same handful of models, so the way each request is governed at the boundary matters more than the model choice itself. DataStrict is deliberately model-agnostic for this reason: the enforcement travels with you across whichever foundation model you build on.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.