Nettle profiled **5 tables and 446 rows**, all classified as dimension tables and named for monthly slices of Indian startup funding and investor data from January to May 2022. Volume is fairly evenly spread across the first four months (115, 98, 96 and 95 rows). January is the largest table at 26% of the total. May is the outlier at 42 rows, about a third of January's count. Completeness is 100%, so every table can be used as delivered without gap-filling. The headline risk is that the 100% referential integrity score is vacuous. No foreign key relationships or validated joins were detected, so there were no constraints to violate and nothing was tested. The five tables are therefore five independent datasets, not a connected model. The strongest capability is clean single-table analysis, run month by month on complete data. The data suits describing each month's funding and investor activity in isolation and comparing row volumes across months. It does not support entity-level linking across tables or a combined view that assumes shared keys. May's lower row count should not be read as a market slowdown until someone confirms whether that file is a partial extract.