Adaptive

Adaptive is a platform designed for businesses that need to deploy large language models (LLMs) in production environments where off-the-shelf models fall short. It provides the tools to evaluate, fine-tune, and serve custom LLMs that are specifically optimized for a company's unique data and performance goals. The core idea is simple: if you can measure a desired outcome—like reducing factual errors or improving response helpfulness—Adaptive can use reinforcement learning to train a model to achieve it.

What makes Adaptive stand out is its focus on practical results over model size. The platform specializes in tuning smaller, open-source models (like Llama or Gemma) to outperform much larger, generic models like GPT-4o on specific tasks. For example, case studies show it reduced hallucinations in a legal chatbot by 25% and achieved a 51% win rate against GPT-4o for a telecom company's document Q&A system. It handles the entire pipeline, from creating synthetic training data to the final model deployment.

This tool is a powerful fit for enterprise teams and AI engineers who are battling the limitations of generic AI APIs. It's particularly valuable for use cases where accuracy, cost-control, and domain-specific knowledge are critical, such as building reliable customer support agents, moderating content in multiple languages, or creating precise text-to-SQL generators. If your business has tried a standard AI model and found it too error-prone or generic, Adaptive provides the machinery to build a smarter, more tailored alternative.