Web scraping to dataset

Some of the most useful datasets do not exist yet and have to be built by hand from dozens of scattered web pages. Someone opens tab after tab, copies a name here and a number there, pastes it into a spreadsheet, and tries to keep the columns consistent as the sources vary. The work is tedious, easy to get wrong, and stale almost as soon as it is finished, and it frequently stretches across a week for anything sizeable. Because it is so painful, the dataset is usually smaller and patchier than the decision really needs. And when the same question comes back a quarter later, the whole grind starts over from nothing.

You describe the dataset you want in plain language, and Ceven runs a wide research pass across many sources at once rather than a single search. It reads across the web, pulls the fields you asked for from each source, and returns a cited brief so you can see where every value came from. AI steps then normalize the findings into consistent rows and columns and write them into Google Sheets, Airtable, Notion, or a BigQuery table. Because the brief carries its citations, the dataset is not a black box; each row can be traced back to the page it came from. The same workflow can be re-run on a schedule, so a one-time scrape becomes a living dataset that keeps up with the sources instead of decaying.

Writing a dataset that other people will build decisions on is consequential, so Ceven holds it at a review gate. The structured rows and their citations are presented for a person to check, who can spot a misread source, drop a low-quality row, and then approve, edit, or reject before the data is published to a shared sheet or warehouse. This keeps a bad source or a parsing slip from silently becoming a fact everyone downstream relies on. Only after approval does the workflow write the reviewed dataset to its destination. Every run is written to an exportable audit trail, so the provenance of the dataset, and who signed off on it, is always recoverable.

You can start free with no credit card, describe the dataset you need in plain language, and point Ceven at where it should land, whether that is Google Sheets, Airtable, Notion, or BigQuery. Ceven builds the workflow across a library of more than a thousand tools, so the same research can feed a market brief or a competitor watch without extra plumbing. Because the source data stays on the web and in your own destination, Ceven never becomes the system of record; it runs the research and structuring around your tools. When you want the dataset refreshed, you run the workflow again rather than repeating the manual grind. If you would rather read the findings as prose first, the cited research brief stands on its own before any rows are written.