MaxReward

Prompt engineering is easy to start with but it's not scalable or optimal for long-term performance. We built this product because building a full reinforcement learning (RL)-based fine-tuning pipeline is incredibly complex and resource-intensive, requiring time, infrastructure, and engineering talent that could be better spent elsewhere. What sets our product apart is that it bridges that gap: we make it easy to implement RL-based optimization for repeated tasks where even a smaller, fine-tuned model can outperform a much larger frontier model with a fraction of the cost and latency. We’re most proud of making it simple to get the best performance. With our platform, teams can go beyond prompt hacking and actually deploy high-accuracy, low-cost models that adapt and improve automatically. It's the power of reinforcement learning without the pain of infrastructure.