Pilot
Validate one workflow
Run a representative source sample and inspect both usable results and exceptions. Agree on acceptance criteria before expanding.
Crawl / RAG Knowledge Data
Keep retrieval grounded in source-linked website content
Crawl selected websites into content your retrieval pipeline can chunk, embed and refresh with traceable citations.
- Scope the source collection
- Crawl and normalize page content
- Chunk and index with version-aware source links
Scope
Define the website boundary
Recursive
Collect connected pages
Formats
Choose supported outputs
Sources
Keep page-level provenance
Who uses RAG Knowledge Data
Crawl selected websites into content your retrieval pipeline can chunk, embed and refresh with traceable citations.
01
RAG developers
Define the business question and review the collected evidence.
02
AI platform teams
Use a repeatable process to compare the observations that matter.
03
Knowledge application builders
Connect reliable inputs to the product or research workflow.
04
Integration owners
Map the source output into your existing application and validate required fields.
05
Workflow operators
Monitor exceptions, maintain collection context and review incomplete runs.
Why this workflow breaks at scale
Design around the failure modes that affect a usable business outcome.
Failure 01
The context gets lost
A stale or orphaned chunk can produce an answer that no longer matches its source. Maintain document identity through every refresh.
Keep comparable observationsFailure 02
A run ends without a usable result
A completed crawl can still omit important paths or include irrelevant ones. Reconcile discovered pages with scope and coverage expectations.
Validate the actual outcomeFailure 03
The next team cannot verify the result
Preserve source context and reviewable evidence alongside the result. Define who handles incomplete records before production collection.
Make the handoff traceableThe data your workflow can use
Keep the collection layer and the normalized business record clearly defined.
- source_url
- Source or destination reference retained for traceability.
- section_text
- Relevant source passage retained for review and downstream analysis.
- document_version
- Version context associated with the retained document.
- chunk_ref
- Stable chunk ref assigned or retained by your application.
NORMALIZED RECORD · ILLUSTRATIVE
{
"source_url": "https://example.com/public-source",
"section_text": "Illustrative source passage for review",
"document_version": "v1",
"chunk_ref": "chunk_sample_01"
}Suggested downstream record, not a literal Scrapeless API response. Field extraction and normalization belong in your application.
Recursive website collection with supported content formats such as Markdown, HTML, links and screenshots.
Example in practice. Refresh an assistant's approved product documentation sources before reindexing.
How this workflow runs
A focused sequence from approved input to a verified result.
- 01
Scope the source collection
Start with: Approved documentation URLs, scope rules and freshness requirements. Define the minimum usable result before collection.
- 02
Crawl and normalize page content
Recursive website collection with supported content formats such as Markdown, HTML, links and screenshots. Record failures and partial results separately.
- 03
Chunk and index with version-aware source links
In-scope coverage, duplicate rate and retained document quality. Retain evidence for records that enter the next stage.
Built for the way your team works
Product capabilities support the workflow; your application defines what success means.
- Seed → scope → pages
Collect within a defined scope
Use recursive collection for selected website areas. Start with a small scope and review discovered pages before expanding the crawl.
- Markdown · HTML · links
Choose useful output formats
Crawl supports content formats including Markdown, HTML, links and screenshots. Select the formats required by your consuming application.
- Page → document → source
Keep source identity
Associate each retained document with its URL and capture context. Your pipeline can then maintain versioning and source-linked citations.
- Filter → deduplicate → publish
Prepare content downstream
Apply your own deduplication, content filters and quality checks before indexing or training. Collecting a page is the beginning of dataset preparation.
Plan the cost of your workflow
Crawl documentation describes browser runtime and proxy traffic as cost drivers. Scope, rendering needs and output formats affect consumption.
Try for Free
Production
Measure real consumption
In-scope coverage, duplicate rate and retained document quality.
Scale
Scope the next stage
Bring your workload mix, operating window and observed consumption to discuss the setup that fits your requirements.
These are workload stages, not named subscription plans. Current rates and account limits apply.
Choose how to run the workflow
Compare responsibilities and operating effort using your own representative workload.
Workflow responsibility
With Crawl
Self-managed stack
Manual process
Execution
Scoped website collection
Your team runs and maintains the execution layer.
An operator repeats each collection or browser step.
Business rules
Source permissions, scope, content filtering, versioning, indexing and downstream model use.
Implemented in your application.
Applied by the researcher or operator.
Acceptance
Keep outcome and content checks in your workflow.
Build validation and exception handling.
Review the captured evidence.
Cost planning
Measure product consumption plus downstream processing.
Measure infrastructure and maintenance effort.
Account for time spent per completed task.
Connect it to the stack you already run
Keep business logic and analysis close to the systems that consume the result.
Python
Node.js
Go
Your search or RAG pipeline
Source permissions, scope, content filtering, versioning, indexing and downstream model use. Open the product documentation for current setup and supported options.
Explore more Crawl workflows
Related workflows using the same product, each with its own inputs and acceptance criteria.
AI Training Data
Crawl approved sites into source-linked content that your pipeline can filter, deduplicate and prepare for model training.
You are here
RAG Knowledge Data
Crawl selected websites into content your retrieval pipeline can chunk, embed and refresh with traceable citations.
Enterprise Knowledge Bases
Collect scoped company documentation and public resources, then review and organize the content around your team's information needs.
Content Migration & Site Capture
Crawl approved site sections to capture page content, links and metadata for migration mapping and post-migration checks.
Whole-site Change Monitoring
Repeat a scoped crawl and compare normalized content snapshots to detect additions, removals and meaningful page changes.
Documentation Indexing
Crawl selected documentation paths and preserve page context for navigation-aware search and discoverable technical references.
Market Intelligence Corpora
Crawl approved company and industry sites to give analysts a comparable body of source-linked material for recurring research.
Multimodal Training Data
Collect screenshots alongside page content to build traceable visual-text records for multimodal research and evaluation.
Looking for more Crawl workflows?
Explore more use cases across training data, RAG, documentation search, and website research.
Questions teams ask before they build
Scope the first run with clear expectations.
What do we need to get started?
What does Crawl provide?
Which parts do we build ourselves?
What can make this workflow misleading?
What boundaries should we plan around?
How should we evaluate a pilot?
Put Crawl to work for your team
Bring your sources, expected crawl jobs and acceptance criteria. Start with a focused pilot, then expand on evidence.