Your domain expertise.
Your custom models.
In your control.
Specialized intelligence, delivered by custom-training frontier models with deep domain knowledge.
Custom, state-of-the-art AI, from R&D to business solutions.
Extensive custom model training.
Tailored models for specialized functions across finance, healthcare, manufacturing, and public sector use cases.
100+
Custom models in production
A world-class AI training stack.
Leverage the proven, production-grade training stack, codebase, and pipelines that delivered Mistral’s state-of-the-art models.
25+
SOTA LLMs
Proven enterprise partnerships.
Jointly built AI centers of excellence within enterprises, fostering long-term independence, autonomy, and expertise.
10+
AI labs co-created
Tailored, co-trained, and run by you.
Turn your proprietary data into a domain‑fluent LLM—trained and tuned to behave exactly as needed on your most important projects.
Enterprise-grade control.
Privacy controls, versioned checkpoints, drift alerts, and explainability keep models accurate and always audit‑ready.
Trainable anywhere, deployable everywhere.
Run anywhere—on‑prem, public or private cloud, or fully on‑device, optimized for your infrastructure.
Customization from fine tuning to full pre-training.
Build, align, and evolve large models using the same methods that power Mistral AI’s frontier systems. Every stage, from knowledge injection to live adaptation, is hardened for scale, control, and reproducibility.
Knowledge integration
Extend foundational models with proprietary data and domain expertise to create true domain specialists.
- Supervised and full fine-tuning integrate expert knowledge directly into weights
- Parameter-efficient methods such as LoRA, QLoRA, and adapters enable modular updates at scale
- Multimodal alignment fuses text, code, vision, and structured data into unified reasoning models
Behavior alignment
Shape cognition with human and synthetic feedback.
- RLHF and DPO refine model judgment and preference alignment
- Role and policy conditioning constrain reasoning to organizational and operational parameters
Computational optimization
Engineer for deterministic, high-throughput operation.
- Quantization, pruning, and distillation compress models without sacrificing reasoning integrity
- Speculative decoding, mixed precision, and on-prem orchestration push throughput to the limits of hardware
- Caching and scheduling frameworks ensure predictable latency across distributed clusters
Continuous reinforcement
Close the loop between production and research.
- Drift detection and reward modeling identify where reasoning degrades
- Active learning and human-in-the-loop retraining continuously refine capability
- Synthetic data generation expands coverage and hardens model robustness
Knowledge integration
Extend foundational models with proprietary data and domain expertise to create true domain specialists.
- Supervised and full fine-tuning integrate expert knowledge directly into weights
- Parameter-efficient methods such as LoRA, QLoRA, and adapters enable modular updates at scale
- Multimodal alignment fuses text, code, vision, and structured data into unified reasoning models
Behavior alignment
Shape cognition with human and synthetic feedback.
- RLHF and DPO refine model judgment and preference alignment
- Role and policy conditioning constrain reasoning to organizational and operational parameters
Computational optimization
Engineer for deterministic, high-throughput operation.
- Quantization, pruning, and distillation compress models without sacrificing reasoning integrity
- Speculative decoding, mixed precision, and on-prem orchestration push throughput to the limits of hardware
- Caching and scheduling frameworks ensure predictable latency across distributed clusters
Continuous reinforcement
Close the loop between production and research.
- Drift detection and reward modeling identify where reasoning degrades
- Active learning and human-in-the-loop retraining continuously refine capability
- Synthetic data generation expands coverage and hardens model robustness
Level up your AI initiatives.
Mission-critical intelligence.
Build models that make high-impact decisions under uncertainty—detecting fraud, predicting failures, and managing systemic risk at scale.
Domain-deep modeling.
Engineer expert systems that internalize the complexity of advanced fields—from materials science and finance to seismology and aerospace.
Deploy models that orchestrate vast, interdependent systems—optimizing logistics, energy networks, and infrastructure in real time.
Frontier research.
Push beyond established architectures to design and train new foundation models, exploring emergent reasoning and multimodal understanding.
High-performance deployment.
Run dense inference and fine-tuning workloads on-prem or at the edge, with strict control over latency, efficiency, and data sovereignty.
Enable your teams to train, scale, and operate large models end-to-end, leveraging the same production-proven systems behind Mistral’s breakthroughs.
Transform model intelligence.
Build expert systems that reason across complex fields and modalities.
Incorporate proprietary datasets
Train domain-specialized large models
Expand reasoning and multimodal understanding
Control model behavior.
Precisely align models to organizational and operational constraints
Enforce compliance and internal policy
Condition behavior by role or objective
Constrain reasoning and response style
Deploy with precision.
Operate inference workloads at industrial scale under tight latency and reliability constraints
Run models in autonomous and robotic systems
Execute high-frequency inference loops
Maintain deterministic performance across infrastructure
Evolve continuously.
Close the loop between production feedback and model performance
Detect drift and degradation in live systems
Retrain with human-in-the-loop feedback
Generate and integrate synthetic data for robustness
Helping organizations that solve the world's most challenging problems.
Helsing
Helsing and Mistral AI have formed a strategic partnership to develop next-generation AI systems for European defence. This collaboration focuses on Vision-Language-Action models, enhancing defence platforms' ability to understand their environment, communicate with operators, and make rapid, reliable decisions in complex scenarios.
Singapore's Ministry of Defence (MINDEF)
MINDEF, Defence Science and Technology Agency (DSTA), and DSO National Laboratories (DSO) partner with Mistral AI to co-develop generative AI models to augment the SAF’s decision support capabilities in areas such as mission planning. The collaboration focuses on fine-tuned Mistral models and developing new mixture-of-experts (MoE) models, with support from AI Singapore for the local operating context.