AI output evaluation refers to the process of assessing the quality, accuracy, and safety of AI-generated outputs. This typically involves structured testing workflows where human reviewers analyze model responses, detect errors, and measure model performance across different tasks.

We support evaluation workflows for multiple types of AI systems, including large language models (LLMs), conversational AI, code generation models, AI agents, computer vision models, and multimodal AI systems.

Data annotation focuses on labeling raw data used for model training. AI evaluation focuses on analyzing model outputs to measure performance, detect failures, and identify areas for improvement after or during model training.

Reviewers may evaluate response quality, verify factual accuracy, detect hallucinations, assess reasoning correctness, test safety compliance, or analyze edge-case behavior in AI systems.

Yes. Our evaluation workflows combine trained reviewer networks with structured scoring guidelines, allowing projects to scale from pilot testing tasks to large evaluation datasets.

Yes. We support adversarial prompt testing, harmful content detection, and policy compliance evaluation to help identify safety risks before AI systems are deployed.

Yes. Many teams begin with a pilot phase to validate evaluation criteria, scoring guidelines, and workflow design before scaling the evaluation process.