Migration Copilot governed AI for Indian schools

Core workflows and features

The operational work behind the promise

1. 01Upload the file you already haveStart from the CSV or XLSX export your current system or office spreadsheet produces, rather than reformatting it into a prescribed template first.
2. 02Suggested column mappingGet a proposed mapping from your file's columns to student fields, so the tedious first pass is done and the reviewer's job is correction.
3. 03Correct the map yourselfOverride any suggested mapping, because the person who knows what a column called 'Grd' means is the school's own administrator.
4. 04Row-level validationSee which rows would fail and why, per row and per field, before deciding whether to fix the source file or proceed without them.
5. 05Preview before commitReview exactly what the import would create, so the commit is a decision made against evidence rather than a hopeful button press.
6. 06Transactional commitCommit the reviewed batch through a controlled path that records the run, so an import is a traceable event and not an untracked bulk insert.
7. 07Import provenance on every recordStamp imported students with their import run, so months later you can tell a migrated record from one created in the office.
8. 08Quiet mode over the batchKeep non-essential outbound messaging held while the school verifies the batch, so a legacy dataset cannot trigger a wave of parent messages.
9. 09Batch rollbackUndo a batch that turned out wrong instead of unpicking hundreds of records by hand, and re-run the import once the source file is corrected.
10. 10Numbers assigned by the school's own schemeLet imported students take admission numbers from the same allocator every other create path uses, so migration cannot fork the numbering.