TamedTable | AI ETL in Natural Language
Talk to your data.
Load tabular data, say what you want, and watch it happen. AI ETL: no formulas, no code.
Open Web App<br>View on GitHub
No API key? Take a guided tour: it replays real sessions, free.
TamedTable
> add a Category column
ProductPriceCategory
Wireless Air Fryer86.30kitchen<br>Deluxe Bluetooth Speaker188.98electronics<br>Mini Fleece Hoodie258.10clothing
Every data preparation job, in natural language
Transform with context
The AI understands context, so it cleans, enriches, classifies, and validates: things no formula can.
See everything
See changes as they happen. Nothing is hidden: TamedTable is source-available, and runs on your own API keys.
Reuse and automate
Every change saves as steps you replay on new data, or export as a Python script. Runs in browser and CLI.
Read many formats
Open CSV, JSONL, Parquet, and Arrow files: local, from a URL, or a bundled sample.
How it works
Paste an API key<br>We work out the provider, models, and cost.
Open a file<br>CSV, JSONL, Parquet, or Arrow. Local or from a URL.
Say what you want<br>Works in any language: English, Chinese, etc.
Watch changes<br>Dislike changes? Undo anything.
Automate<br>You made a recipe; replay it on new data.
Lazy AI execution
Preview for cents, run for real when you're sure. AI steps fill the page you are looking at; the rest waits until you ask.
open a 25,000-row fileStarts as a shuffled sample; saving keeps the original order<br>"add a Category column"Fills the rows on screen right away; the pager marks what's pending<br>Run on all rowsRows, cost, and time shown before you commit; finished rows are kept
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Clean up
Fix messy fields the way a person would. The AI reads each row's context before deciding.
"normalize the phone numbers"Dialing prefix inferred from regions<br>"make the country names consistent"Folds UK / England / United Kingdom into one<br>"fix the capitalization of names"Handles McDonald, van der Berg<br>"clean up the birth dates"Knows 03/04 is March in the US, April in the EU
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Enrich & extract
Add what isn't there yet. The model fills the gaps and pulls structure out of free text.
"split the address into Street, City, and Zip"Structures whatever mess they typed<br>"fill the country from the city column"Knows Osaka → Japan<br>"add the industry for each company"Inferred from company names<br>"extract the amount and date from the memo, refunds negative"Pulled from free text, refunds signed
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Classify
Sort rows by what they mean. The AI reads what each row is about.
"label each ticket as billing, bug, or feature"Reads the ticket's meaning<br>"classify the ticket sentiment into positive, negative and neutral"Reads the verdict, even in mixed tickets<br>"sort the titles by seniority"Ranks senior → junior<br>"split customers into men, women, and unknown"Gender inferred from first names
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Validate
Catch the wrong-looking rows before anyone else does.
"flag emails that look fake"bill.gates@microsoft.com probably didn't sign up<br>"flag any impossible birth date, like Feb 30th or year 1873"Dates that parse can still be impossible<br>"check the city matches the country"Paris, Japan gets a second look<br>"flag prices that seem wrong"Catches the missing zero before Reddit does
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Process language
Summarize, translate, and classify any language.
tap the micSay any command out loud, hands-free<br>"tag the language of every comment"Language detected per row<br>"translate the comments to English"From any source language<br>"add a one-line Summary for each comment"Even the 500-word rant<br>"normaliza los números de teléfono"Ask in any language, voice or text
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Be exact
The everyday spreadsheet jobs are still here; deterministic and repeatable.
"Remove the duplicate rows"Dedupe<br>"Join with join-country-codes.csv on Country to add ISO and Region"Join two tables<br>"Show only customers in Europe"Filter rows: on the column the join just added<br>"Pivot Quarter into columns, with Revenue as the value"Reshape tall to wide<br>"Sort by Q4, descending"Sort or top-N
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Load, save & reuse
Your work is yours to keep. Load from anywhere, save the data or the recipe, undo anything.
a file or a URLLoads CSV, JSONL, Parquet, Arrow from web or CLI<br>:save clean.csvSave data to CSV, JSONL, Parquet, or Arrow<br>:save-flow tidy.flowSave transformations as a TamedTable script<br>:save-py tidy.pySave transformations as a Python script<br>:undo / :redoUndo/redo any change
Trustworthy by design
A repeatable recipe
Each request becomes a small spec you can read. Deterministic steps run in a real SQL engine, and the same recipe replays on next month's file.
Undo anything
Every change lands in the history. Step back one change or all of them; the original file on your disk is never touched.
Leave anytime
Export any recipe as a plain Python script and run it without TamedTable. Your data stays in open formats; there is no lock-in to defend.
Tested & benchmarked
CI replays 606 test scenarios on...