Judged by meaning, not string matching
Deciding whether a complaint is about delivery or product quality requires understanding the sentence, not checking whether it contains a keyword. That is the part formulas and regular expressions cannot reach.
AI Table and Spreadsheet Processing
Process spreadsheets row by row against your rules: classify, extract, rewrite, translate, and fill gaps at scale.
There is a class of work that formulas cannot do and a script is not worth writing for: tagging thirty thousand pieces of user feedback, splitting a free-text address into administrative levels, deciding by meaning which category each row belongs to. All of it requires reading the content before judging it. Formulas only match characters, and the volume defeats manual work.
Table processing fills that gap. You state the rule; the agent reads each row, applies it, and writes the result into a new column. Rules are described in plain language rather than functions. When it finishes you get counts of how many rows succeeded and how many could not be judged, with the undecidable ones flagged rather than filled with a guess.
What it can do
Tens of thousands of rows per run
Large sheets are batched automatically with progress preserved, so rows are not dropped as volume grows. You get success, failure, and undecidable counts at the end.
Rules in plain language
No formulas or code. Describe the criterion, give an example or two, and it applies that standard across the sheet.
Uncertain rows are flagged
When the content does not support a decision, the row is marked for review with a reason instead of being assigned a plausible category. In bulk work this silent failure mode does far more damage than a visible error.
How it works
- Upload and read the structureExcel or CSV is uploaded, headers and column meanings identified, and the columns to process and the destination confirmed.
- Align on the ruleYou describe the rule, the agent restates its understanding and runs it on a small sample for you to check before the full pass.
- Run the full sheetRows are processed with progress tracked; anomalous rows are recorded individually without halting the job.
- Deliver and verifyA new sheet is produced with statistics on how many rows succeeded and how many await review, so spot checks and follow-up are straightforward.
Worked examples
Classifying user feedback
Classify these 30,000 feedback entries as delivery, product quality, support attitude, price, or other. Flag anything you cannot decide.
A new classification column with per-category counts and a set of flagged rows, each with a stated reason it could not be decided.
Translating a whole sheet
Translate this product description sheet into English and Japanese, keep terminology consistent, and leave the model number column untouched.
Two new translation columns with terminology unified across the sheet and the specified column preserved exactly.
Frequently asked questions
How many rows can it handle?
Tens of thousands in a single run, with very large sheets batched and progress preserved. Final counts by status make it easy to confirm nothing was skipped.
Do I need to write formulas or code?
No. Describe the rule in ordinary language; one or two examples help it match your standard.
Could it damage my original sheet?
Results are written to new columns or a new file. Source data is not overwritten.
What happens if something fails midway?
Problem rows are recorded separately without stopping the rest, and you are told at the end which rows need another pass.