Automate spreadsheet cleanup with rules and optional AI assistance
Operations teams lose days to the same manual fixes: trimming names, standardising phone formats, parsing dates from mixed locales and splitting compound address fields. This utility packages those transforms as reusable rules you can preview on a sample before you touch a full export. Deterministic operations such as case changes or column copies can be reasoned about locally, while AI-assisted intent detection routes through the server so the model can suggest operations in plain language. Read the usage meter in the UI before you run large batches and keep sensitive columns out of AI flows when your data policy requires on-device handling only.
Cleanup workflow
- Paste or load a small sample and describe the outcome you want.
- Review suggested rules; edit column targets and operations explicitly.
- Preview transformed rows before you apply to the full grid.
- Export results back to Sheets or CSV and document rules for the next vendor drop.
When to trust rules versus AI suggestions
Prefer explicit deterministic rules for compliance-sensitive fields: emails, government ids and monetary amounts should use well-known transforms you can audit. Use AI intent help when exploring messy text columns with inconsistent phrasing, then lock in concrete rules once you agree on the pattern. The API path applies rate limits and plan gates so automated jobs do not surprise finance with runaway token usage. Profile the same file locally in the Data Profiler first so you know which columns actually need help versus which already match the contract.
Privacy, retention and handoff
Treat server-side transforms like any other cloud processor: minimum necessary columns, redact where possible and avoid patient, cardholder or classified fields unless counsel approves. After cleanup, validate shapes with the CSV ⇄ JSON Converter when downstream systems expect JSON, or rebuild INSERT statements with the CSV → SQL Import Helper. Store the final rule JSON beside the ticket so the next analyst does not re-derive intent from scratch.
Freemium expectations
The MVP demonstrates a Marketplace-ready funnel: free tiers prove value on smaller sheets, Pro tiers raise row ceilings and unlock advanced operations flagged in the UI. If a rule fails mid-batch, roll back from your source export rather than layering fixes blindly. Pair manual review with profiling metrics so you can show stakeholders before-and-after null rates, not only a prettier grid.
Keep the source file immutable
Treat cleanup as a transform from a raw input to a clean output, never an edit in place. Keep the original export untouched in storage or the ticket and write the cleaned version to a new file, so you can always re-run from scratch when a rule turns out to be wrong. Layering manual fixes on top of earlier fixes is how a column quietly ends up double-trimmed or a date parsed twice. Pair the immutable source with a saved rule set and you have a reproducible pipeline: the same input plus the same rules equals the same output, which is the property an auditor or a future teammate will thank you for.