Mistral AI Announces Forge to Help Enterprises Build Proprietary AI Models

Mistral AI, making frontier AI accessible to everyone, has introduced Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge.

Most AI models today are trained on public data and optimized for general-purpose tasks, while enterprises rely on deeply embedded internal knowledge, ranging from engineering standards to operational workflows.

Forge bridges this gap by enabling organizations to train AI models on proprietary data, aligning them with internal systems, policies, and decision-making processes.

Mistral AI has already partnered with leading organizations such as ASML, DSO National Laboratories Singapore, Ericsson, European Space Agency, Home Team Science and Technology Agency, and Reply to build models trained on proprietary data powering complex systems and next-generation technologies.

Training Models on Institutional Knowledge

Forge enables enterprises to build AI models that internalize domain-specific knowledge by training on internal documentation, codebases, structured data, and operational records, capturing the vocabulary, reasoning patterns, and constraints unique to each organization.

This allows models and agents to operate using internal terminology and align with enterprise workflows. Forge supports key stages of the model lifecycle:

-> Pre-training – Builds domain-aware models using large internal datasets.

-> Post-training – Refines behavior for specific tasks and environments.

-> Reinforcement learning – Aligns models with internal policies, evaluation criteria, and real-world decision-making.

Together, these capabilities move enterprises beyond generic AI, enabling systems that reflect true institutional intelligence.

Control and Strategic Autonomy

AI adoption raises critical questions around control of models, data, and intellectual property. Forge enables enterprises to retain full ownership by training models on proprietary datasets and governing them through internal policies, evaluation standards, and operational requirements.

This ensures organizational knowledge remains controlled and securely embedded within AI systems, an essential requirement in regulated environments where compliance and governance are paramount.

By supporting models built on internal knowledge and deployed within enterprise infrastructure, Forge enables greater strategic autonomy as AI becomes integral to core operations.

Custom Models Make Enterprise Agents Reliable

Enterprise agents must go beyond generating answers; they need to navigate internal systems, use tools accurately, and operate within organizational constraints.

Custom models enable this by embedding a deep understanding of internal environments. Trained on domain-specific data, these models allow agents to interpret internal terminology, follow workflows, and understand relationships across systems and data sources.

As a result, tool selection becomes more precise, multi-step workflows more reliable, and decisions aligned with internal policies and business logic.

This shifts agents from simple assistants to operational components, capable of executing tasks, coordinating across tools, and supporting complex processes with greater speed and accuracy.

Support for Multiple Model Architectures

Forge supports both dense and mixture-of-experts (MoE) architectures, allowing enterprises to optimize for performance, cost, and operational needs. Dense models deliver strong general capability, while MoE models enable large-scale performance with lower latency and compute efficiency.

Support for multimodal inputs further allows models to learn from text, images, and other data formats, enabling broader applicability across enterprise use cases.

Agent-First by Design

Forge is built with agents as primary users, not an afterthought. Autonomous agents like Mistral Vibe can fine-tune models, optimize hyperparameters, schedule jobs, and generate synthetic data to improve performance.

Forge continuously monitors metrics to prevent regressions, while handling infrastructure and providing proven data pipelines and training methods from Mistral AI. This allows even agents to customize models using simple natural language, streamlining the entire development process.

Continuous Improvement Through Reinforcement Learning and Evaluation

Enterprise environments evolve continuously, regulations shift, systems update, and new data emerges. Forge is built for ongoing adaptation, not one-time training.

Reinforcement learning pipelines allow models to improve using feedback from internal evaluations and workflows, while evaluation frameworks test performance against enterprise benchmarks, compliance rules, and domain-specific tasks before deployment.

The result is a dynamic model lifecycle that enables continuous improvement rather than static deployment.

Examples of Enterprise Applications

Forge can be applied across a wide range of enterprise workflows:

Government – Models trained on languages, policies, and regulatory frameworks enable reliable AI agents for policy analysis, public services, and operational planning.

Financial Services – Models aligned with compliance frameworks and risk procedures ensure outputs match internal governance and regulatory requirements.

Software Engineering – Models trained on proprietary codebases and standards improve tasks like implementation, debugging, and system design with deeper contextual understanding.

Manufacturing – Models built on engineering data and maintenance records support diagnostics, design analysis, and operational decisions.

Enterprises at Scale – Agents powered by internally trained models can navigate company knowledge systems, execute workflows, and assist across complex operations with greater accuracy.

Across all use cases, the goal remains consistent: enabling models and agents to operate within the organization’s domain context.

Build Your Own Frontier Models with Forge

AI models are becoming a core layer of enterprise infrastructure. As agents integrate into operations, encoding institutional knowledge into model behavior is increasingly critical.

Forge enables organizations to build and continuously improve models trained on proprietary data and aligned with internal workflows. These models power AI systems that operate using enterprise-specific terminology, processes, and constraints.

Over time, this transforms AI from an external tool into a strategic asset that evolves alongside organizational knowledge and expertise.