Applied Compute offers a specialised AI supercloud platform tailored for enterprises that prefer to build and refine their own AI models rather than relying on off-the-shelf solutions. The platform is engineered to support the entire lifecycle of open models, from initial training and fine-tuning to efficient inference and continuous improvement in production. It provides the necessary infrastructure and developer-friendly tools to enable organisations to leverage their proprietary data and expertise to create highly customised and performant AI agents.
The core offering, AC2, focuses on delivering high-performance training and inference capabilities. This allows customers to encode their specific quality standards and domain knowledge directly into model training, leading to AI agents that are precisely aligned with their product and use-case requirements. The platform supports advanced techniques like large-scale reinforcement learning (RL) within custom agent harnesses, enabling the development of sophisticated AI applications across various industries.
What differentiates Applied Compute is its emphasis on sovereignty and control over AI models. By providing the infrastructure for post-training and inference with custom harnesses and configurations, it empowers engineers to ensure that the data they create genuinely enhances model performance. This approach not only leads to higher accuracy and reduced inference costs but also results in sovereign models that continuously improve as an organisation's data and tools evolve. Notable customers include DoorDash, Cognition, Clay, Mercor, Harvey, and Semgrep, who have reported significant improvements in model quality, cost efficiency, and deployment speed.
The platform is particularly beneficial for companies dealing with sensitive data or unique operational requirements, such as legal tech, cybersecurity, and logistics, where generic models may fall short. Applied Compute acts as a strategic partner, offering deep expertise in training, evaluation, and task design, helping clients transform their internal data into state-of-the-art models that perform exceptionally well in real-world production settings.