Electronic health records (EHRs) are a treasure trove of information detailing oncology patients’ management and outcomes, but unlocking meaningful insights can be challenging. Structured data such as ICD10 codes and medication dispenses are useful but often lack the context crucial for a deeper understanding. Unstructured data, including physician notes, radiology and pathology reports, are rich with insights but must be laboriously reviewed, and at scale, this becomes infeasible. Here we describe the development, validation, and scaling of automated Natural Language Processing (NLP) and Large Language Model (LLM) pipelines and go on to show real‑world applications of our ‘AI‑enhanced’ data model, including matching patients to clinical trial criteria with high accuracy and defining therapy‑related adverse events. Using John Snow Labs’ NLP frameworks, we developed a comprehensive pipeline that performed optical character recognition, entity extraction, assertion detection, and relationship mapping from physician notes, radiology, pathology, and genetic reports focusing on temporal, anatomical, oncological, pathological, and treatment‑related entities and connections. Our dataset contained over 1.4 million physician notes and approximately 1 million PDF reports and scans. The technical implementation leveraged both classical LSTM‑based models and transformer‑based approaches (BioBERT).