ほとんどの企業向けAIプロジェクトは、技術が不足しているから失敗するのではなく、使われているモデルが自社のビジネスを理解していないために失敗します。モデルは多くの場合、インターネット上で訓練されており、数十年にわたる社内文書、ワークフロー、組織知識には基づいていません。
そのギャップこそが、企業顧客を基盤にビジネスを展開してきたフランスのAIスタートアップ Mistral が機会を見出すポイントです。火曜日に同社は Mistral Forge を発表しました。これは企業が自社データで訓練したカスタムモデルを構築できるプラットフォームです。 Mistral はこのプラットフォームを Nvidia GTC、Nvidiaの年次テクノロジーカンファレンスで発表しました。今年はエンタープライズ向けのAIとエージェント型モデルに特に焦点を当てています。
It’s a pointed move for Mistral, a company that has built its business on corporate clients while rivals OpenAI and Anthropic have soared ahead in terms of consumer adoption. CEO Arthur Mensch says Mistral’s laser focus on the enterprise is working: The company is on track to 年次経常収益が10億ドルを超える見込みだ。
エンタープライズ分野に力を入れる大きな理由の一つは、企業にデータとAIシステムのより大きなコントロールを提供することだと、Mistralは語る。
“What Forge does is it lets enterprises and governments customize AI models for their specific needs,” Elisa Salamanca, Mistral’s head of product, told TechCrunch.
Several companies in the enterprise AI space already claim to offer similar capabilities, but most focus on fine-tuning existing models or layering proprietary data on top through techniques like retrieval augmented generation (RAG). These approaches don’t fundamentally retrain models; instead, they adapt or query them at runtime using company data.
Mistral, by contrast, says it is enabling companies to train models from scratch. In theory, this could address 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 reliance on third-party model providers, avoiding risks like model changes or deprecation.
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Forge customers can build their custom models using Mistral’s wide library of open-weight AI models, which includes small models such as the recently introduced Mistral Small 4. According to Mistral co-founder and chief technologist, Timothée Lacroix, Forge can help unlock more value out of its existing models.
“The trade-offs that we make when we build smaller models is that they just cannot be as good on every topic as their larger counterparts, and so the ability to customize them lets us pick what we emphasize and what we drop,” Lacroix said.
Mistral advises on which models and infrastructure to use, but both decisions stay with the customer, Lacroix said. And for teams that need more than guidance, Forge comes with Mistral’s team of forward-deployed engineers who embed directly with customers to surface the right data and adapt to their needs — a model borrowed from the likes of IBM and Palantir.
“As a product, Forge already comes with all the tooling and infrastructure so you can generate synthetic data pipelines,” Salamanca said. “But understanding how to build the right evals and making sure that you have the right amount of data is something that enterprises usually don’t have the right expertise for, and that’s what the FDEs bring to the table.”
Mistral has already made Forge available to partners, including Ericsson, the European Space Agency, Italian consulting company Reply, and Singapore’s DSO and HTX. Early adopters also include ASML, the Dutch chipmaker that led Mistral’s Series C round last September at a €11.7 billion valuation (approximately $13.8 billion at the time).
These partnerships are emblematic of what Mistral expects Forge’s main use cases to be. According to Mistral’s chief revenue officer Marjorie Janiewicz, these include governments who need to tailor models for their language and culture; financial players with high compliance requirements; manufacturers with customization needs; and tech companies that need to tune models to their code base.
