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Frontier models for critical domains
The LLM Data Company is training models in data scarce verticals
The LLM Data Company trains specialized frontier models for production use in critical domains, focusing on post-trained models that perform better and cheaper than general-purpose frontier models. They emphasize end-to-end training inside a production harness and a data-centric approach for domain-specific agents.
They develop post-training models tailored to customer use cases by training interior to the production harness, using a Curriculum autoresearch platform to curate tasks and rewards for on-policy RL, and deploying models within the customer’s harness to reduce the sim2real gap and hardware costs. They offer frontier models designed to be deployed across thousands of harnesses, with a focus on domain-specific performance and scalable, production-ready data for training specialized agents.
Who it’s for: enterprises and teams replacing generalist models with domain-specialist production agents in practical, high-stakes domains such as math/code-related tasks and other verifiable domains where data is bottlenecked
research releases and a public latest release (Kos-1), mention of production-ready approaches and partnerships with frontier agent companies, ongoing focus on enterprise replacement of generalist models
Write, version and execute evals and RL rewards
The LLM Data Company builds tooling to write, version, and execute evals for models and agents, with a focus on high-signal, aligned evaluators and fast iteration. Its product, doteval, provides an evals-as-code workspace to version evals across checkpoints, export specs for RL training, and benchmark model performance for frontier AI teams and complex tasks.
Artificial Specialized Intelligence
Frontier coding data for training and evaluating LLMs
