Ekrem Sarı

Ekrem is an AI Researcher at AIMultiple, focusing on intelligent automation, AI Agents, and RAG frameworks.

Professional Experience

During his tenure as an Assessor at Yandex, he evaluated search results using proprietary frameworks and automated protocols. He implemented QA testing through data annotation, relevance scoring, and user intent mapping across 10,000+ queries monthly, while conducting technical assessments including performance monitoring and spam detection using ML feedback loops.

Research Interest

At AIMultiple, his research is centered on the performance and benchmarking of end-to-end AI systems. He contributes to a wide range of projects, including Retrieval-Augmented Generation (RAG) optimization, extensive Large Language Model (LLM) benchmarking, and the design of agentic AI frameworks. Ekrem specializes in developing data-driven methodologies to measure and improve AI technology performance across critical metrics like accuracy, efficiency, cost, and scalability.

His analysis covers the entire technology stack, from foundational components like embedding models and vector databases to the infrastructure required for deploying AI agents, such as remote browser solutions and web automation platforms.