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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

🎙️ Feature spotlight 🧩 Fully flexible data curation flows While curating and refining test datasets from logs and test runs, you can now reference and modify any data point from a trace or test run entry; without being limited to predefined fields like input or output. Use Maxim’s DSL in the

The last few years of neural speech synthesis have been wild. Flow matching models, diffusion transformers, and insanely natural TTS systems keep raising the bar. The catch? Most of these models expect to see the whole audio sequence at once . While this is great for offline generation, but if you’

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

Observability in AI applications differs fundamentally from traditional application monitoring. While conventional systems deal with deterministic request-response cycles, AI applications involve multi-turn conversations, complex reasoning chains, multiple model invocations, and retrieval operations - all of which need visibility for debugging, optimization, and understanding system behavior. Maxim's observability platform