Today we will introduce: forgesystems for businesses Build frontier-grade AI models based on your own knowledge.
Most currently available AI models are trained primarily on publicly available data. They are designed to perform well across a wide range of tasks. But companies operate using internal knowledge such as engineering standards, compliance policies, codebases, operational processes, and long-standing organizational decisions.
Forge bridges the gap between general AI and enterprise-specific needs. Instead of relying on extensive public data, organizations can train models that understand the internal context embedded in systems, workflows, and policies to tailor AI to their unique operations.
Mistral AI already partners with some of the world’s leading organizations, including: ASML, DSO National Laboratory of Singapore, Ericsson, european space agency, Home Team Science and Technology Agency (HTX) Singaporeand reply Train models on unique data that powers the most complex systems and future-defining technologies.
Training models based on organizational knowledge.
Forge allows companies to build models that internalize their domain knowledge. Organizations can train models on large volumes of internal documents, codebases, structured data, and operational records. During training, a model learns the vocabulary, inference patterns, and constraints that define its environment.
This enables teams to develop models and agents that use internal terminology to reason and understand enterprise workflows. Forge supports modern training approaches across several stages of a model’s lifecycle.
- Pre-training allows organizations to learn from large internal datasets to build domain-aware models.
- Post-training methods allow teams to tailor model behavior to specific tasks and environments.
- Reinforcement learning helps organizations align models and agents with internal policies, metrics, and operational goals, improving agent performance in real-world environments such as complex orchestration, tool usage, and decision-making.
Together, these capabilities enable companies to go beyond typical AI behavior and develop models that reflect organizational intelligence.
Control and strategic autonomy.
For many organizations, the introduction of AI raises questions about the management of models, data, and long-term intellectual property. Forge allows businesses to build models under their control. Models can be trained using your own datasets and managed using internal policies, metrics, and operational requirements.
This allows organizations to maintain control over how knowledge is encoded and used by AI systems. This level of control is important in a regulated environment. Companies must ensure that their models reflect compliance requirements, operational constraints, and internal governance frameworks.
By enabling organizations to build models based on their own knowledge and operate within their own infrastructure environments, Forge enables greater strategic autonomy as AI becomes part of core enterprise systems.
Custom models make enterprise agents reliable.
Enterprise agents need to do more than just generate answers. They must navigate internal systems, use tools correctly, and make decisions within the constraints of the organization.
Custom models make this possible by giving agents a deeper understanding of the environment in which they operate. Instead of relying on general reasoning, agents powered by domain-trained models can interpret internal terminology, follow operational procedures, and understand how different systems and data sources relate to each other.
This changes the actual agent behavior. Tool selection becomes more precise. Multi-step workflows are more reliable. Decisions reflect internal policies and business logic rather than general assumptions.
As a result, agents go beyond mere assistance and begin to function as operational components of enterprise systems that can perform tasks more accurately and quickly, coordinate across tools, and support complex processes.
Support for multiple model architectures.
Forge provides the flexibility to support both high-density and mixed-expertise (MoE) architectures. This allows organizations to optimize performance, cost, and operational constraints. Dense models provide powerful general functionality across a wide range of enterprise tasks, while MoE allows you to run very large models more efficiently. It provides comparable functionality with lower latency and compute costs than similarly sized dense models. Forge also supports multimodal input if needed, allowing models to learn from text, images, and other data formats.
Agent-first design
We built Forge for code agents first, rather than as an afterthought, because code agents are becoming the primary users of developer tools. Autonomous agents like Mistral Vibe can use this to fine-tune models, find optimal hyperparameters, schedule jobs, and generate synthetic data for hillclimb evaluation. Throughout the process, Forge monitors metrics to ensure that the model does not regress on the benchmarks of interest. Forge handles the infrastructure and includes proven recipes for data pipelines and Mistral AI’s unique training methods, so anyone, including agents, can customize models by simply writing plain English.
Continuous improvement with reinforcement learning and evaluation.
Enterprise environments are constantly evolving. Regulations will change. The system will be 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 from internal evaluations and operational workflows.
This allows your team to improve the model over time and align the output with your organization’s goals. Evaluation frameworks allow companies to test models against internal benchmarks, compliance rules, and domain-specific tasks before deploying them in production.
The result is a model lifecycle that supports continuous improvement rather than static deployment.
Example of an enterprise application.
Organizations can apply Forge across different types of enterprise workflows.
government agency Build trained models for different languages and dialects, policy frameworks, regulatory documents, and administrative procedures. This allows AI agents to be more reliable when addressing policy analysis, public service delivery, and operational planning, while reflecting institutional mandates and governance requirements.
financial institution Train models on compliance frameworks, risk procedures, and regulatory documents. This allows AI systems to produce output that is consistent with internal governance policies.
software team You can train models based on your own codebase and development standards. The real value comes from forming models that perform exceptionally well at specific engineering tasks that drive productivity and quality within your company. Models trained on proprietary repositories and development standards can better understand internal abstractions, patterns, and architectural choices. Post-mortem training on preferred workflows such as implementation, debugging, migration, review, and system design support can provide output that is more context-aware, more aligned with internal practices, and more useful throughout the software development lifecycle.
Manufacturer You can train models based on engineering specifications, operational data, and maintenance records. These models can support diagnostics, design analysis, and operational decision-making.
large company You can deploy agents built on models trained on internal knowledge systems. These agents can use company documents, business records, and past decisions to assist employees throughout complex workflows. The underlying custom model understands your organization’s terminology and knowledge structure, allowing agents to obtain information and perform tasks more accurately and quickly.
In either case, the purpose is the same. This means allowing the model and the agents built on top of it to operate within the organization’s domain context.
Build your own Frontier models using Forge.
AI models are becoming the foundational layer of enterprise infrastructure. As organizations integrate AI agents into their core operations, the ability to encode organizational knowledge into model behavior becomes increasingly important.
Forge enables enterprises to build and continuously improve models that are trained on their own data and tailored to their operational context. These models can power AI systems and agents that operate using your organization’s terminology, processes, and constraints. This approach allows organizations to treat AI models not just as external tools, but as strategic assets that evolve with knowledge, processes, and expertise.
If you’re ready to explore what it means for your organization to build AI based on your own knowledge, sign up to learn more about Forge.
