Data first, scrapers second

Many scrapers. One dataset. You approve what's true.

Scrapbase turns scraping around. Define the data you want, plug in AI agents, vibe-coded scripts and integrations side by side, and validate every row they bring in a dashboard made for it.

Free trial, 30 rowsNo API keys to bringRuns in isolated sandboxes

The inversion

Most tools start from the scraper. Scrapbase starts from the data.

The usual way
  1. 01 Write a scraper for one site
  2. 02 Export a CSV
  3. 03 Clean it by hand in a spreadsheet
  4. 04 Do it all again next week
With Scrapbase
  1. 01 Define the columns you want, and which ones identify an item
  2. 02 Plug in as many scrapers as you like
  3. 03 Review what they bring as pending rows
  4. 04 Every run after that only adds what is new or changed

Every scraper is the same four choices

Whatever does the fetching, you set it up the same way.

  1. Start from

    Nothing, a list you paste, or rows of the dataset that match a condition.

    website is not empty
    latasse-lyon.fr
    —
    slowpour.co
  2. Get data with

    Any provider: an AI agent, a vibe-coded script, web pages, Apify, an import.

    AI agentVibe-codeWeb pagesApifyImport
  3. Results

    New rows, or written back into the rows they came from, empty cells only if you want.

    La Tassehello@latasse-lyon.fr
    Slow Pourteam@slowpour.co
    only into empty cells
  4. Run

    By hand, on a schedule, or automatically when rows match.

    Manual
    Daily 08:00
    When rows match

Multiple AI scrapers

A crew of scrapers working on the same rows

Use the right tool for each source, and as many as you need. They all write into one dataset, so nothing has to be stitched together afterwards.

AI agent

Claude reads the pages, or searches the web, and fills the columns you described.

Vibe-coded scrapers

Describe the source in a chat. The AI writes the script and tests it in a sandbox until it works.

Web pages

CSS selectors, sitemaps, schema.org data and RSS feeds, with JavaScript rendering. No code.

Integrations

Google Maps, Instagram, Amazon, any website, and any Actor of the Apify Store.

Imports & webhooks

JSON APIs, CSV and Google Sheets, n8n, Zapier and Make, or push rows through the API.

They hand work to each other

No workflow to draw. A scraper that fills website wakes up the one that starts from website.

Each scraper remembers the rows it processed and never triggers itself, so chains can't loop.

Google Maps · Apify
starts from nothing → fills name, website
website filled
AI agent · Claude
starts from website → fills email, instagram
instagram filled
Instagram · Apify
starts from instagram → fills followers
namewebsiteemailinstagramfollowers
Café Mokkacafemokka.frbonjour@cafemokka.fr@cafemokka12.4k
Slow Pourslowpour.coteam@slowpour.co@slowpour.lyon3,180
Petit Noirpetitnoir.cafehello@petitnoir.cafe@petitnoir8,902

On the way in

Messy items in, clean rows out

Every item, whichever scraper brought it, is mapped onto your columns and cast to their types. Identity columns recognise the same item across runs and scrapers, so it updates a row instead of duplicating it.

raw itemfrom Web pages
{
"title": " Slow Pour "
"site": "HTTPS://www.SlowPour.co/"
"stars": "4,6 ★"
"tel": "04 78 12 34 56"
}
mapped & cast
nametext
Slow Pour
websiteurl
https://slowpour.co
ratingnumber
4.6
phonephone
+33 4 78 12 34 56
identity check
create a new row
update the existing row
skip, nothing changed

No row has slowpour.co yet

The data dashboard

Nothing counts until someone approves it

Every row a scraper brings lands as pending. Review it in the view that suits the job, alone or with your team.

Coffee shops · Lyon
namewebsiteratingstatus
Café Mokkacafemokka.fr4.7approved
Slow Pourslowpour.co4.6pending
La Tasselatasse-lyon.fr4.4pending
Kopi Cornerkopicorner.fr4.8approved
Petit Noirpetitnoir.cafe4.5pending
Grain de Cafégraindecafe.com4.2approved

Under the hood

Safe to run, and it costs what it uses

Disposable sandboxes

Our servers never fetch the sites you scrape. Each run borrows a fresh machine, runs as an unprivileged user behind a firewall, then gives it back.

One balance, settled per run

Machines, AI tokens, Apify runs and unblocked pages all spend the same credits. A run reserves an estimate, then pays what it really used.

reserved 6427 back to the balance

Sites behind a sign-in

Sign in once, the way you always do. Scrapbase never sees the password.

  1. Sign in on a streamed desktop, 2FA included
  2. The session is saved as a snapshot
  3. Scrapers open pages already signed in

Describe the data. Let the scrapers bring it. Keep only what's right.

Scrapbase· data first, scrapers second
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