The 9 SQL queries against 1,311 rows represent a lightweight data retrieval phase; query execution time is negligible at this scale, placing the compute burden squarely on LLM synthesis stages. This workload is suitable for scheduled runs on a daily or weekly cadence, though on-demand execution remains viable if latency tolerance is 2–5 minutes. To reduce runtime on future iterations, prioritize filtering or sampling the input dataset upstream—reducing row volume before LLM processing will yield the largest efficiency gain, since synthesis cost scales directly with data cardinality rather than query complexity.