About Daniel

Daniel has built and deployed advanced LLM-based systems for document analysis, information extraction, and knowledge processing for US-based companies. His work focuses on transforming unstructured data into reliable, structured outputs that can be safely used in business-critical workflows. He has experience designing LLM pipelines for tasks such as classification, summarization, entity extraction, and retrieval-augmented generation, always with production constraints in mind.

He places strong emphasis on model evaluation and benchmarking, bringing research-level rigor into real-world AI systems. Daniel actively works on hallucination mitigation, prompt and output validation, and systematic testing of LLM behavior across edge cases. His approach bridges applied research and engineering, ensuring that LLM systems are not only powerful but also measurable, predictable, and trustworthy in production environments.