We are a specialized research and education organization focused on the practical challenges of building and deploying artificial intelligence systems in production environments. Our work centers on evaluation methodologies for large language models and generative AI, an area where we have developed deep expertise through direct engagement with the technical challenges facing engineering and product teams.
Our organization emerged from recognizing a critical gap in the AI ecosystem. While tremendous progress has been made in model capabilities, the methods for systematically evaluating these systems have not kept pace. We saw engineering teams struggling with questions that traditional software testing approaches could not answer, and product teams unable to confidently assess whether AI systems were ready for users. This motivated us to focus specifically on evaluation frameworks that bridge the gap between AI research and practical deployment.
Our approach is grounded in real-world application rather than purely academic research. We work closely with teams building AI products across industries, understanding firsthand the constraints and tradeoffs they face. This practical orientation shapes everything we teach, ensuring our educational content addresses actual challenges rather than theoretical ideals. We prioritize frameworks and techniques that work within resource constraints and integrate smoothly into existing development workflows.
We believe that robust evaluation is not just a technical necessity but a foundation for responsible AI development. Our work emphasizes transparency in understanding model behavior, systematic approaches to identifying failures, and clear communication of capabilities and limitations. We advocate for evaluation practices that help teams build AI systems that are not only performant but trustworthy and aligned with user needs.
Our educational philosophy centers on practical implementation. We translate complex evaluation concepts into actionable frameworks that engineers and product managers can apply immediately. Our curriculum design reflects the multidisciplinary nature of modern AI development, bridging technical depth with product thinking and emphasizing collaboration between different roles.
Through our courses and research, we aim to establish evaluation as a core competency for AI practitioners, elevating the standards for how AI systems are tested and validated before reaching production.
