Introducing Forge

Today, we’re introducing Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge.

Most AI models available today are trained primarily on publicly available data. They are designed to perform well across a broad range of tasks. But enterprises operate using internal knowledge: engineering standards, compliance policies, codebases, operational processes, and years of institutional decisions.

Forge bridges the gap between generic AI and enterprise-specific needs. Instead of relying on broad, public data, organizations can train models that understand their internal context embedded within systems, workflows, and policies, aligning AI with their unique operations.

Mistral AI has already partnered with world-leading organizations, like ASML, DSO National Laboratories Singapore, Ericsson, European Space Agency, Home Team Science and Technology Agency (HTX) Singapore, and Reply to train models on the proprietary data that powers their most complex systems and future-defining technologies.

Training models on institutional knowledge.

Forge enables enterprises to build models that internalize their domain knowledge. Organizations can train models on large volumes of internal documentation, codebases, structured data, and operational records. During training, the model learns the vocabulary, reasoning patterns, and constraints that define that environment.

This allows teams to develop models and agents that reason using internal terminology and understand enterprise workflows. Forge supports modern training approaches across several stages of the model lifecycle:

Pre-training allows organizations to build domain-aware models by learning from large internal datasets.

Post-training methods allow teams to refine model behavior for specific tasks and environments.