Evaluation

Since the beginning, HTTP Endpoint-Based Offline Evals have been a core feature of the Maxim platform and a favorite among our users. While our SDKs allow engineers to integrate evaluations directly into their codebase, a purely code-based approach introduces friction, often limiting who can run them and how they are

Introduction Customer support is one of the most impactful use cases for AI agents. A well-designed support agent can handle thousands of inquiries simultaneously, provide instant responses, and maintain context across complex conversations. But how do you ensure your agent actually works before unleashing it on real customers? In this

Introduction Before deploying your AI system to production, you need confidence that it performs well across various scenarios, maintains quality standards, and produces consistent results. This is where offline evaluations become essential. Offline evaluations use curated datasets, scenario simulations, and evaluators to benchmark prompts, workflows, and agents before deployment. They

Sure, your model aced every benchmark, but can you trust it when the stakes are real? Every frontier lab runs alignment post-training before shipping their chat models to the world. The problem? Actually auditing whether this alignment worked can be an absolute nightmare. You're basically trying to find

Generative AI is reshaping how insurers operate and serve their customers. Across sectors like health, life, auto, and property & casualty, insurers are embracing GenAI to enhance customer experience, drive efficiency, and improve decision-making. This shift isn’t just theoretical; over two-thirds of insurers are already using GenAI regularly, and