Fetch public documentation and research pages. Feed the pipeline your models need.

Documentation, reference material, release notes and research pages change over time. Send each URL to ScrapeDrive, get HTML, visible text, Markdown or a screenshot, and process the content under your own data and model rules.

Start for free Try a documentation page

500 credits after you confirm your email. No card.

Public technical documentation and research becoming a cleaned source-linked dataset.

curl "https://sync.scrapedrive.com/api/v1/scrape" \
  -H "Content-Type: application/json" \
  -d '{
    "api_key": "YOUR_KEY",
    "url": "https://docs.example/guide/getting-started",
    "result_type": "page_markdown",
    "proxy_pool": "datacenter"
  }'

What comes back

Response body: The documentation page converted to Markdown.
X-Sdrive-Credits: The credits charged. A plain datacenter request costs 5.
X-Sdrive-Job-Id: The job id. The same job appears in your dashboard job history.
A request that ends without a result: Costs 0 credits.

What AI teams fetch, and what they build.

AI product and research teams use permitted public pages as inputs to knowledge systems, datasets and evaluations. Fetching is the first step, not the whole pipeline.

Documentation pages
Title, headings, body text, links and version
What they build. Retrieval collections for search and assistants
Release notes
Product, version, changes and date
What they build. Product change timelines
Research pages
Title, authors, abstract and links
What they build. Literature indexes
Reference and editorial pages
Main text, date, language and source
What they build. Permitted corpus inputs
Public discussion pages
Topic, post text, date and links
What they build. Evaluation and research samples

Pages teams fetch	What is on the page	What they build from it
Documentation pages	Title, headings, body text, links and version	Retrieval collections for search and assistants
Release notes	Product, version, changes and date	Product change timelines
Research pages	Title, authors, abstract and links	Literature indexes
Reference and editorial pages	Main text, date, language and source	Permitted corpus inputs
Public discussion pages	Topic, post text, date and links	Evaluation and research samples

Four ways AI teams use fetched pages.

In each workflow, ScrapeDrive performs the fetch. Rights checks, filtering, chunking, labeling and model use belong to your pipeline.

Keep a retrieval system current with approved documentation

An assistant that answers from last quarter's docs gives last quarter's answers. Product teams fetch a known set of documentation URLs again and reprocess what changed.

Who does it: AI product and knowledge platform teams
Pages they fetch: Documentation, API reference and release note pages from sources you approved
What they build: A refresh queue that sends changed pages to chunking, embedding and indexing
ScrapeDrive features that help: Output formats JavaScript rendering Async requests
Stays with your team: Approving sources, chunking, embedding, indexing and deciding how often to refresh

Refresh queue prepared by your code

Page	What your code found
/guide/start	Content changed
/api/reference	Version updated
/release-notes	New entry
/guide/legacy	Page removed

Your pipeline spots changes by comparing each fetch with the saved copy.

Gather dataset content from sources you may use

Research teams build datasets from many pages. Fetching a page does not grant permission to use it, and it does not make the page good training data.

Who does it: Machine learning research teams
Pages they fetch: Pages from sources your team has checked for rights and terms
What they build: A dataset that passed your rights, language, duplicate and quality checks
ScrapeDrive features that help: Output formats Async requests Cost controls
Stays with your team: Rights and privacy review, main content selection, deduplication, filtering and quality review

Dataset pipeline

1Fetch the pageScrapeDrive
2Check rights and termsYour team
3Select the main contentYour code
4Check language and remove duplicatesYour code
5Review quality and approveYour team

ScrapeDrive performs only the first step.

Save page states for repeatable model and agent tests

An evaluation is only repeatable when its input stays the same. Evaluation teams capture selected pages and screenshots, then write tasks against that saved evidence.

Who does it: Evaluation and agent testing teams
Pages they fetch: Selected public pages that a model or agent will be tested on
What they build: An evaluation set with the saved page, a screenshot, the capture time and an expected result written by a person
ScrapeDrive features that help: Webpage screenshots Browser waits Output formats
Stays with your team: Writing tasks and expected results, versioning the set and deciding when to capture again

Evaluation record

Target URL: Saved
Captured at: Your timestamp
Page version: Your hash
Expected result: Written by a person

Your team creates and reviews labels and expected outcomes.

Follow public releases and research in your field

Model providers and tool vendors change their documentation and features often. Product and research teams fetch those pages and compare them over time.

Who does it: Product strategy and research teams
Pages they fetch: Public release notes, documentation and research publication pages
What they build: A change timeline that links every summary to the page it came from
ScrapeDrive features that help: Output formats Async requests Browser waits
Stays with your team: Deciding which changes matter, resolving product names and reviewing summaries before sharing them

Change timeline your application keeps

Release note: Feature changed
Documentation: New parameter added
Research page: New paper linked

Your analysts decide what a change means.

Keep engineering time for data quality and model work, not page-fetching infrastructure.

JavaScript content
If your team builds it. Operate browser workers
With ScrapeDrive. Turn on JavaScript rendering
Request routing
If your team builds it. Maintain proxy infrastructure
With ScrapeDrive. Select a route or use Auto
Late-loading sections
If your team builds it. Write readiness and timing logic
With ScrapeDrive. Add a browser wait
Many known URLs
If your team builds it. Maintain long-running workers
With ScrapeDrive. Queue asynchronous requests
Readable page content
If your team builds it. Convert returned HTML
With ScrapeDrive. Request visible text or Markdown

Page problem	If your team builds it	With ScrapeDrive
JavaScript content	Operate browser workers	Turn on JavaScript rendering
Request routing	Maintain proxy infrastructure	Select a route or use Auto
Late-loading sections	Write readiness and timing logic	Add a browser wait
Many known URLs	Maintain long-running workers	Queue asynchronous requests
Readable page content	Convert returned HTML	Request visible text or Markdown

What ScrapeDrive handles, and what stays with you.

ScrapeDrive returns page content. What you may do with it and how you prepare it stay with your team.

ScrapeDrive does

Fetches each documentation, release note, research or reference URL you send
Returns HTML, visible text or Markdown at the same price
Runs a browser for documentation sites that render with JavaScript
Blocks images, styles, fonts and ad scripts by default in browser runs
Captures screenshots for visual evaluation
Queues many URLs as async jobs with webhook delivery
Charges 0 credits when a request ends without a result

Your team does

Checking rights, terms and privacy for every source
Selecting the main content and removing duplicates
Chunking, embedding and labeling
Building and versioning datasets and evaluation sets
Deciding how and when the content reaches a model

Need agreed sources collected and delivered on a schedule? Managed scraping builds and runs that for you. See managed scraping

Features AI pipelines tend to need.

Start with a plain request. Add browser, routing or waiting settings only when the page needs them.

Output formats
Choose HTML, visible text or Markdown for the next step in your pipeline.
See the feature, Output formats
JavaScript rendering
Run page scripts before capture for documentation sites built in the browser.
See the feature, JavaScript rendering
Async requests
Queue many known URLs and receive each result at a webhook.
See the feature, Async requests
Webpage screenshots
Save the rendered page for visual review and evaluation.
See the feature, Webpage screenshots

Questions AI teams ask

Start with one documentation page.

Paste a page URL into the Playground and see exactly what ScrapeDrive returns.

Start for free Open the Playground

500 credits after you confirm your email. No card.

Fetch public documentation and research pages. Feed the pipeline your models need.

What AI teams fetch, and what they build.

Four ways AI teams use fetched pages.

Keep a retrieval system current with approved documentation

Gather dataset content from sources you may use

Save page states for repeatable model and agent tests

Follow public releases and research in your field

Keep engineering time for data quality and model work, not page-fetching infrastructure.

What ScrapeDrive handles, and what stays with you.

ScrapeDrive does

Your team does

Features AI pipelines tend to need.

Output formats

JavaScript rendering

Async requests

Webpage screenshots

Questions AI teams ask

Can I embed the Markdown as it comes back?

Does fetching a page give me the right to train on it?

Can one request fetch a whole documentation site?

How many pages can I fetch at the same time?

What does a documentation page cost?

Start with one documentation page.