Mistral AI Releases Forge

Mistral Forge

Build your own frontier models

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.
• Reinforcement learning helps organizations align models and agents with internal policies, evaluation criteria, and operational objectives while improving agentic performance in real environments, like complex orchestration, tool use, and decision-making.

Together, these capabilities allow enterprises to move beyond generic AI behavior and develop models that reflect institutional intelligence.

Control and strategic autonomy.

For many organizations, AI adoption raises questions about control over models, data, and long-term intellectual property. Forge allows enterprises to build models that remain under their control. Models can be trained using proprietary datasets and governed using internal policies, evaluation standards, and operational requirements.

This allows organizations to retain control over how their knowledge is encoded and used by AI systems. In regulated environments, this level of control is critical. Enterprises must ensure that models reflect compliance requirements, operational constraints, and internal governance frameworks.

By allowing organizations to build models grounded in their own knowledge and operated within their own infrastructure environments, Forge enables a higher degree of strategic autonomy as AI becomes part of core enterprise systems.

Custom models make enterprise agents reliable.

Enterprise agents must do more than generate answers. They need to navigate internal systems, use tools correctly, and make decisions within the constraints of the organization.

Custom models make this possible by providing agents with a deeper understanding of the environment in which they operate. Instead of relying on generic reasoning, agents powered by domain-trained models can interpret internal terminology, follow operational procedures, and understand how different systems and data sources relate to one another.

This changes how agents behave in practice. Tool selection becomes more precise. Multi-step workflows become more reliable. Decisions can reflect internal policies and business logic rather than generic assumptions.

The result is agents that move beyond simple assistance and begin to function as operational components of enterprise systems capable of executing tasks, coordinating across tools, and supporting complex processes with greater accuracy and speed.

Support for multiple model architectures.

Forge offers flexibility with support for both dense and mixture-of-experts (MoE) architectures. This lets organizations optimize for performance, cost, and operational constraints. Dense models provide strong general capability across a wide range of enterprise tasks, while MoE enables very large models to run more efficiently; delivering comparable capability with lower latency and compute cost than a dense model of similar scale. Forge also supports multimodal inputs where required, allowing models to learn from text, images, and other data formats.

Agent-first by design

Code agents are becoming the primary users of developer tools, so we built Forge for them first, not as an afterthought. An autonomous agent like Mistral Vibe can use it to fine-tune models, find optimal hyperparameters, schedule jobs, and generate synthetic data to hill-climb evals. Throughout the process, Forge monitors metrics to make sure the model isn't regressing on the benchmarks you care about. Because Forge handles infrastructure and includes battle-tested recipes for data pipelines and Mistral AI's own training methods, anyone, including agents, can customize a model just by writing plain English.

Continuous improvement through reinforcement learning and evaluation.

Enterprise environments evolve constantly. Regulations change. Systems are updated. New data becomes available. Forge is designed for continuous adaptation rather than one-time training. Organizations can use reinforcement learning pipelines to refine model behavior using feedback derived from internal evaluations and operational workflows.

This allows teams to improve models over time and align outputs with organizational objectives. Evaluation frameworks allow enterprises to test models against internal benchmarks, compliance rules, and domain-specific tasks before deploying them into production environments.

The result is a model lifecycle that supports ongoing improvement rather than static deployment.

Examples of enterprise applications.

Organizations can apply Forge across many types of enterprise workflows.

Government agencies can build models trained for different languages and dialects, policy frameworks, regulatory texts, and administrative procedures. This allows AI agents to be reliable when working on policy analysis, public service delivery, and operational planning while reflecting institutional mandates and governance requirements.

Financial institutions can train models on compliance frameworks, risk procedures, and regulatory documentation. This allows AI systems to produce outputs consistent with internal governance policies.

Software teams can train models on proprietary codebases and development standards. The real value comes from shaping models to perform exceptionally well on the specific engineering tasks that drive productivity and quality inside the company. A model trained on proprietary repositories and development standards can better understand internal abstractions, patterns, and architectural choices. When post-trained for priority workflows like implementation, debugging, migration, review, or system design support, it can provide outputs that are more context-aware, more consistent with internal practices, and more useful across the software development lifecycle.

Manufacturers can train models on engineering specifications, operational data, and maintenance records. These models can support diagnostics, design analysis, and operational decision-making.

Large enterprises can deploy agents built on models trained on internal knowledge systems. These agents can use company documentation, operation