Mistral bets on ‘build-your-own AI’ as it takes on OpenAI, Anthropic in the enterprise

Most enterprise AI projects fail not because companies don’t have the technology, but because the models they use don’t understand their business. The models are often trained on the Internet, rather than decades of internal documents, workflows and institutional knowledge.

Mistral, the French AI startup, sees opportunities in that gap. On Tuesday, the company announced Mistral Forge, a platform that allows companies to build custom models trained on their own data. Mistral announced the platform at Nvidia GTC, Nvidia’s annual technology conference, which this year focuses heavily on AI and agentic modeling for enterprises.

It’s a targeted move for Mistral, a company that has built its business on enterprise customers while rivals OpenAI and Anthropic have made a big leap forward in consumer adoption. CEO Arthur Mensch says Mistral’s laser focus on the business is working: the company is on track Surpass $1 billion in annual recurring revenue this year.

A big part of doubling down on entrepreneurship is giving companies more control over their data and their AI systems, Mistral says.

“What Forge does is allow companies and governments to tailor AI models to their specific needs,” Elisa Salamanca, Mistral’s head of product, told TechCrunch.

Several enterprise AI companies already claim to offer similar capabilities, but most focus on refining existing models or layering proprietary data on top through techniques like Retrieval Augmented Generation (RAG). These approaches do not fundamentally reduce models; instead, they modify or query them at runtime using business data.

Mistral, on the other hand, says it allows companies to train models from scratch. In theory, this could overcome some of the limitations of more common approaches, for example better handling of non-English or highly domain-specific data and greater control over model behavior. It could also allow companies to train agentic systems using reinforcement learning and reduce dependence on third-party model providers, avoiding risks such as model changes or deprecation.

Forge customers can build their custom models using Mistral’s wide library of open-weight AI models, including small models like the recently introduced Mistral Small 4. According to Mistral co-founder and chief technologist Timothée Lacroix, Forge can help extract more value from its existing models.

“The trade-off we make when we build smaller models is that they simply can’t be as good on every subject as their larger counterparts, and so the ability to customize them allows us to choose what we emphasize and what we drop,” Lacroix said.

Mistral advises on which models and infrastructure to use, but both decisions remain with the customer, Lacroix said. And for teams that need more than guidance, Forge comes up with Mistral’s team of forward-thinking engineers that work directly with customers to surface the right data and adapt to their needs – a model borrowed from companies like IBM and Palantir.

“As a product, Forge already comes with all the tools and infrastructure so you can generate synthetic data pipelines,” Salamanca said. “But understanding how to build the good evaluates And making sure you have the right amount of data is something that companies typically don’t have the right expertise for, and that’s what the FDEs are bringing to the table.”

Mistral has already made Forge available to partners including Ericsson, the European Space Agency, Italian consultancy Reply and Singapore’s DSO and HTX. The early adopters also include ASML, the Dutch chipmaker that led the charge Mistral series C finalized last September at a valuation of €11.7 billion (approximately $13.8 billion at the time).

These partnerships are emblematic of what Mistral expects Forge’s key use cases to be. According to Mistral Chief Revenue Officer Marjorie Janiewicz, these include governments needing to tailor models to their language and culture; financial players with high compliance requirements; manufacturers with customization needs; and technology companies that need to tailor models to their code base.