Developed an aggressive table extraction feature for the run-llama/llama_cloud_services repository, focusing on enhancing LlamaParse’s ability to detect tabular data within input documents. The work centered on introducing a tunable aggressive_table_extraction flag and updating the base parsing payload schema to support this new control, allowing users to balance precision and recall according to their needs. Implemented entirely in Python with an emphasis on API development, the solution prioritized payload compatibility and end-to-end integration. While no major bugs were addressed during this period, the primary contribution improved automation and reliability for downstream analytics workflows that depend on accurate table extraction.