Connect Olostep and JJ_TESTING to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Olostep
Choose an Action
Automatically do this!
Get Input Data
Enable Integrations or automations with these events of Olostep and JJ_TESTING
Enable Integrations or automations with these events of Olostep and JJ_TESTING
Actions
Scrape Website
Fetch and extract content from a public webpage into selected formats (Markdown, HTML, text, JSON, screenshot, or PDF) using optional settings like country, wait time, parser, and image removal.
Get Scrape
Fetch a scrape by ID
Generate AI Answer
Search web and return answer
Start Web Crawl
Start web crawl and notify
Get Crawl Details
Get crawl status and details
Create Map
Create a site map by discovering pages on a website and returning the found URLs, limited and filtered by the provided options.
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Frequently Asked Questions
To start, connect both your Olostep and JJ_TESTING accounts to viaSocket. Once connected, you can set up a workflow where an event in Olostep triggers actions in JJ_TESTING (or vice versa).
Absolutely. You can customize how Olostep data is recorded in JJ_TESTING. This includes choosing which data fields go into which fields of JJ_TESTING, setting up custom formats, and filtering out unwanted information.
The data sync between Olostep and JJ_TESTING typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Olostep and JJ_TESTING. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Olostep
Olostep is a web data platform that enables developers and AI systems to search, crawl, and extract structured data from the public web at scale. It provides APIs for real-time web scraping, site crawling, batch data extraction, and AI-ready search with source grounding, making it ideal for powering AI agents, research workflows, and data pipelines.
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