Find the right domain experts for your AI project

TrainAI’s native-speaking specialists deliver culturally nuanced, domain-specific data that enhances multilingual reasoning, localization accuracy, and inclusivity across 500+ language pairs and variants. With RWS’s 65 years of experience in language, locale, and cultural expertise, we bring the domain-specific authenticity global AI systems need to perform reliably in every market, supported by proven frameworks for scalable, high-quality delivery.

Our domain experts surface and label complex, real-world edge cases that standard benchmarks miss, capturing the reasoning, context, and nuance that make or break model performance in production. Through the work we do for leading frontier AI models, TrainAI brings unique insights, emerging best practices and stress-testing methodologies that push models to their real-world limits before deployment.

Our experts go beyond scoring outputs. They help teams by identifying failure patterns, diagnosing root causes, and mapping precise data fixes that improve accuracy and reliability. Through TrainAI University and our expert training ecosystem, we turn complex evaluation guidelines into comprehensive training, knowledge sharing and high-impact feedback to close performance gaps faster. 

Agentic AI demands deeper oversight – not just grading outputs – but understanding how agents reason, plan, and act. Our AI domain experts evaluate every stage of an agent’s reasoning and tool use to ensure logical consistency, accuracy, and safety across workflows. With hands-on experience building agentic AI infrastructure using MCP, TrainAI supports clients in creating, curating, annotating and validating data for agentic AI, including detailed reasoning audits and safety evaluations. 

Our flexible workforce model lets AI teams scale the right expertise exactly when they need it. TrainAI’s vetted community of domain experts can be deployed and scaled up or down by skill, language, or domain while ensuring consistent quality and continuity across regions and project types. Agile deployment and hybrid engagement models (e.g., employees vs. freelancers vs. contractors) mean we can start fast, adapt quickly, and deliver reliable outcomes in both research and production environments.