Blog
How BuildShip Orchestrates Complex Web Scraping and Workflow Automation with Apify
Tutorial
·
Jun 5, 2025
In a world where critical data lives behind dynamic websites, web scraping has become an essential superpower. Whether you're gathering competitive intel, automating research, or feeding AI models with fresh content, tools like Apify make it possible to extract structured data from platforms like YouTube, LinkedIn, TikTok, and Google Maps without building scrapers from scratch.
But scraping alone isn’t enough. You need workflows that can process, transform, and deploy that data into useful outcomes.
In a world where critical data lives behind dynamic websites, web scraping has become an essential superpower. Whether you're gathering competitive intel, automating research, or feeding AI models with fresh content, tools like Apify make it possible to extract structured data from platforms like YouTube, LinkedIn, TikTok, and Google Maps without building scrapers from scratch.
But scraping alone isn’t enough. You need workflows that can process, transform, and deploy that data into useful outcomes.
In a world where critical data lives behind dynamic websites, web scraping has become an essential superpower. Whether you're gathering competitive intel, automating research, or feeding AI models with fresh content, tools like Apify make it possible to extract structured data from platforms like YouTube, LinkedIn, TikTok, and Google Maps without building scrapers from scratch.
But scraping alone isn’t enough. You need workflows that can process, transform, and deploy that data into useful outcomes.



Automating YouTube Quiz Generation: Web Scraping with Apify and BuildShip
In this guide, we'll show you how to combine Apify's powerful web scraping capabilities with BuildShip's workflow automation to extract video captions and transform them into structured quiz questions (no coding required).
What You'll Learn
Web scraping traditionally requires significant technical expertise, but platforms like Apify make it accessible through pre-built "actors" (their term for web scrapers). When combined with BuildShip's visual workflow builder, you can create powerful automation pipelines that:
Extract data from popular platforms (YouTube, TikTok, LinkedIn, Google Maps)
Process the extracted content with AI
Structure the results for use in your applications
Deploy the entire workflow as an API endpoint
Let's build a practical example: an automated system that generates quiz questions from any YouTube video.
Setting Up Your YouTube Quiz Generator
Our workflow will:
Accept a YouTube video URL as input
Use Apify to extract the video's captions
Process the captions with Anthropic's Claude to generate quiz questions
Structure the quiz in JSON format for easy integration with any frontend
Step 1: Create a New BuildShip Workflow
Start by creating a new workflow in BuildShip:
Log into your BuildShip account and navigate to the dashboard
Click "Create New Workflow"
Name your workflow (e.g., "YouTube Quiz Generator")
Add a string input parameter named "videoURL"
Step 2: Add the Apify YouTube Captions Extractor
Apify offers specialized actors for different platforms. For our use case, we'll use their YouTube caption extractor:
Open the nodes library in BuildShip
Navigate to the Apify integration group
Add the "Get YouTube Captions" node to your workflow
Configure your Apify API key (you can find this in your Apify account under Settings → API and Integrations)
Replace the default URL with your workflow input:
{{inputs.videoURL}}
Note: BuildShip currently offers Apify data extraction nodes for YouTube, Instagram, Reddit, and TikTok, with more platforms coming soon.
Step 3: Process the Caption Data
The Apify node returns captions as an array of timestamped text segments. We need to combine these into a single text block:
Add the "Extract and Join by Key" utility node
Configure it to:
Take the output array from the Apify node
Extract and join the "text" field from each array item
This gives us the complete video transcript as a single string, perfect for passing to an AI model.
Step 4: Generate Quiz Questions with Claude
Now we'll use Anthropic's Claude to transform the transcript into quiz questions:
Add the "Claude AI Chat" node
Configure it to use your project credits (or add your Anthropic API key)
Add the following instructions:
You are a quiz generation expert. I'll provide you with captions from a YouTube video. Your task is to: 1. Extract 5 key concepts, facts, or events from the content 2. Create a multiple-choice question for each (with 4 options per question) 3. Include the correct answer after each question Format each question as: QUESTION: [the question] CHOICES: A. [option] B. [option] C. [option] D. [option] ANSWER: [correct letter]
Set the prompt to use the output from your "Extract and Join by Key" node
Step 5: Structure the Quiz in JSON Format
Finally, we'll convert the quiz into structured JSON for easy integration with any frontend:
Add the "JSON Generator" node from OpenAI
Configure it to use your project credits
Set the input to use the output from the Claude node
Define the JSON schema:
{ "quiz": [ { "question": "string", "choices": "string", "answer": "string" } ] }
For advanced settings, select GPT-4 as the model
Testing Your Workflow
Let's test our workflow with a real YouTube video:
Click "Test Workflow" in BuildShip
Enter a YouTube video URL
Run the test and review the output

You should receive a structured JSON response containing your quiz questions, choices, and answers:
{ "quiz": [ { "question": "What platform does the workflow use to extract captions from YouTube videos?", "choices": "A. BuildShip\\nB. Apify\\nC. Claude\\nD. OpenAI", "answer": "B" }, { "question": "What AI model is used to generate the quiz questions?", "choices": "A. GPT-4\\nB. DALL-E\\nC. Claude\\nD. Stable Diffusion", "answer": "C" }, // Additional questions... ] }
Troubleshooting Common Issues
If you encounter errors during setup, check these common issues:
Invalid input type: Ensure the Apify node is configured to accept an array, not an object
Model selection errors: For the JSON Generator node, use GPT-4 instead of older models
Missing field names: Double-check your JSON schema to ensure all fields are properly named
Beyond YouTube: Expanding Your Web Scraping Capabilities
While our example focuses on YouTube captions, Apify's ecosystem offers actors for dozens of platforms:
E-commerce data: Extract product information from Amazon, eBay, or Shopify
Social media insights: Gather posts, comments, and engagement metrics from Instagram, TikTok, or LinkedIn
Local business data: Collect business information, reviews, and locations from Google Maps
Each of these can be integrated with BuildShip to create powerful automation workflows. For a step by step video tutorial on this please click below:
Advanced Apify Features to Explore
To get even more from your Apify + BuildShip integration:
Proxy management: Configure proxies through Apify to avoid rate limiting and IP blocks
Storage options: Use Apify's dataset storage for large scraping jobs
Actor marketplace: Explore pre-built actors for specialized scraping tasks
Custom actors: Develop your own actors for unique data extraction needs
Conclusion
Web scraping is no longer just a developer’s tool - it’s becoming a core part of modern automation stacks. With platforms like Apify providing powerful, ready-made scrapers and BuildShip turning complex workflows into visual building blocks, anyone can unlock and repurpose data across the web.
Whether you’re generating quizzes from video captions, pulling reviews from Google Maps, or transforming social media content into insights, the combo of Apify and BuildShip puts that power in your hands. This example is just one piece of what’s possible. Now that you’ve seen how quickly you can go from raw data to a usable API, imagine what else you could automate.
Automating YouTube Quiz Generation: Web Scraping with Apify and BuildShip
In this guide, we'll show you how to combine Apify's powerful web scraping capabilities with BuildShip's workflow automation to extract video captions and transform them into structured quiz questions (no coding required).
What You'll Learn
Web scraping traditionally requires significant technical expertise, but platforms like Apify make it accessible through pre-built "actors" (their term for web scrapers). When combined with BuildShip's visual workflow builder, you can create powerful automation pipelines that:
Extract data from popular platforms (YouTube, TikTok, LinkedIn, Google Maps)
Process the extracted content with AI
Structure the results for use in your applications
Deploy the entire workflow as an API endpoint
Let's build a practical example: an automated system that generates quiz questions from any YouTube video.
Setting Up Your YouTube Quiz Generator
Our workflow will:
Accept a YouTube video URL as input
Use Apify to extract the video's captions
Process the captions with Anthropic's Claude to generate quiz questions
Structure the quiz in JSON format for easy integration with any frontend
Step 1: Create a New BuildShip Workflow
Start by creating a new workflow in BuildShip:
Log into your BuildShip account and navigate to the dashboard
Click "Create New Workflow"
Name your workflow (e.g., "YouTube Quiz Generator")
Add a string input parameter named "videoURL"
Step 2: Add the Apify YouTube Captions Extractor
Apify offers specialized actors for different platforms. For our use case, we'll use their YouTube caption extractor:
Open the nodes library in BuildShip
Navigate to the Apify integration group
Add the "Get YouTube Captions" node to your workflow
Configure your Apify API key (you can find this in your Apify account under Settings → API and Integrations)
Replace the default URL with your workflow input:
{{inputs.videoURL}}
Note: BuildShip currently offers Apify data extraction nodes for YouTube, Instagram, Reddit, and TikTok, with more platforms coming soon.
Step 3: Process the Caption Data
The Apify node returns captions as an array of timestamped text segments. We need to combine these into a single text block:
Add the "Extract and Join by Key" utility node
Configure it to:
Take the output array from the Apify node
Extract and join the "text" field from each array item
This gives us the complete video transcript as a single string, perfect for passing to an AI model.
Step 4: Generate Quiz Questions with Claude
Now we'll use Anthropic's Claude to transform the transcript into quiz questions:
Add the "Claude AI Chat" node
Configure it to use your project credits (or add your Anthropic API key)
Add the following instructions:
You are a quiz generation expert. I'll provide you with captions from a YouTube video. Your task is to: 1. Extract 5 key concepts, facts, or events from the content 2. Create a multiple-choice question for each (with 4 options per question) 3. Include the correct answer after each question Format each question as: QUESTION: [the question] CHOICES: A. [option] B. [option] C. [option] D. [option] ANSWER: [correct letter]
Set the prompt to use the output from your "Extract and Join by Key" node
Step 5: Structure the Quiz in JSON Format
Finally, we'll convert the quiz into structured JSON for easy integration with any frontend:
Add the "JSON Generator" node from OpenAI
Configure it to use your project credits
Set the input to use the output from the Claude node
Define the JSON schema:
{ "quiz": [ { "question": "string", "choices": "string", "answer": "string" } ] }
For advanced settings, select GPT-4 as the model
Testing Your Workflow
Let's test our workflow with a real YouTube video:
Click "Test Workflow" in BuildShip
Enter a YouTube video URL
Run the test and review the output

You should receive a structured JSON response containing your quiz questions, choices, and answers:
{ "quiz": [ { "question": "What platform does the workflow use to extract captions from YouTube videos?", "choices": "A. BuildShip\\nB. Apify\\nC. Claude\\nD. OpenAI", "answer": "B" }, { "question": "What AI model is used to generate the quiz questions?", "choices": "A. GPT-4\\nB. DALL-E\\nC. Claude\\nD. Stable Diffusion", "answer": "C" }, // Additional questions... ] }
Troubleshooting Common Issues
If you encounter errors during setup, check these common issues:
Invalid input type: Ensure the Apify node is configured to accept an array, not an object
Model selection errors: For the JSON Generator node, use GPT-4 instead of older models
Missing field names: Double-check your JSON schema to ensure all fields are properly named
Beyond YouTube: Expanding Your Web Scraping Capabilities
While our example focuses on YouTube captions, Apify's ecosystem offers actors for dozens of platforms:
E-commerce data: Extract product information from Amazon, eBay, or Shopify
Social media insights: Gather posts, comments, and engagement metrics from Instagram, TikTok, or LinkedIn
Local business data: Collect business information, reviews, and locations from Google Maps
Each of these can be integrated with BuildShip to create powerful automation workflows. For a step by step video tutorial on this please click below:
Advanced Apify Features to Explore
To get even more from your Apify + BuildShip integration:
Proxy management: Configure proxies through Apify to avoid rate limiting and IP blocks
Storage options: Use Apify's dataset storage for large scraping jobs
Actor marketplace: Explore pre-built actors for specialized scraping tasks
Custom actors: Develop your own actors for unique data extraction needs
Conclusion
Web scraping is no longer just a developer’s tool - it’s becoming a core part of modern automation stacks. With platforms like Apify providing powerful, ready-made scrapers and BuildShip turning complex workflows into visual building blocks, anyone can unlock and repurpose data across the web.
Whether you’re generating quizzes from video captions, pulling reviews from Google Maps, or transforming social media content into insights, the combo of Apify and BuildShip puts that power in your hands. This example is just one piece of what’s possible. Now that you’ve seen how quickly you can go from raw data to a usable API, imagine what else you could automate.
Automating YouTube Quiz Generation: Web Scraping with Apify and BuildShip
In this guide, we'll show you how to combine Apify's powerful web scraping capabilities with BuildShip's workflow automation to extract video captions and transform them into structured quiz questions (no coding required).
What You'll Learn
Web scraping traditionally requires significant technical expertise, but platforms like Apify make it accessible through pre-built "actors" (their term for web scrapers). When combined with BuildShip's visual workflow builder, you can create powerful automation pipelines that:
Extract data from popular platforms (YouTube, TikTok, LinkedIn, Google Maps)
Process the extracted content with AI
Structure the results for use in your applications
Deploy the entire workflow as an API endpoint
Let's build a practical example: an automated system that generates quiz questions from any YouTube video.
Setting Up Your YouTube Quiz Generator
Our workflow will:
Accept a YouTube video URL as input
Use Apify to extract the video's captions
Process the captions with Anthropic's Claude to generate quiz questions
Structure the quiz in JSON format for easy integration with any frontend
Step 1: Create a New BuildShip Workflow
Start by creating a new workflow in BuildShip:
Log into your BuildShip account and navigate to the dashboard
Click "Create New Workflow"
Name your workflow (e.g., "YouTube Quiz Generator")
Add a string input parameter named "videoURL"
Step 2: Add the Apify YouTube Captions Extractor
Apify offers specialized actors for different platforms. For our use case, we'll use their YouTube caption extractor:
Open the nodes library in BuildShip
Navigate to the Apify integration group
Add the "Get YouTube Captions" node to your workflow
Configure your Apify API key (you can find this in your Apify account under Settings → API and Integrations)
Replace the default URL with your workflow input:
{{inputs.videoURL}}
Note: BuildShip currently offers Apify data extraction nodes for YouTube, Instagram, Reddit, and TikTok, with more platforms coming soon.
Step 3: Process the Caption Data
The Apify node returns captions as an array of timestamped text segments. We need to combine these into a single text block:
Add the "Extract and Join by Key" utility node
Configure it to:
Take the output array from the Apify node
Extract and join the "text" field from each array item
This gives us the complete video transcript as a single string, perfect for passing to an AI model.
Step 4: Generate Quiz Questions with Claude
Now we'll use Anthropic's Claude to transform the transcript into quiz questions:
Add the "Claude AI Chat" node
Configure it to use your project credits (or add your Anthropic API key)
Add the following instructions:
You are a quiz generation expert. I'll provide you with captions from a YouTube video. Your task is to: 1. Extract 5 key concepts, facts, or events from the content 2. Create a multiple-choice question for each (with 4 options per question) 3. Include the correct answer after each question Format each question as: QUESTION: [the question] CHOICES: A. [option] B. [option] C. [option] D. [option] ANSWER: [correct letter]
Set the prompt to use the output from your "Extract and Join by Key" node
Step 5: Structure the Quiz in JSON Format
Finally, we'll convert the quiz into structured JSON for easy integration with any frontend:
Add the "JSON Generator" node from OpenAI
Configure it to use your project credits
Set the input to use the output from the Claude node
Define the JSON schema:
{ "quiz": [ { "question": "string", "choices": "string", "answer": "string" } ] }
For advanced settings, select GPT-4 as the model
Testing Your Workflow
Let's test our workflow with a real YouTube video:
Click "Test Workflow" in BuildShip
Enter a YouTube video URL
Run the test and review the output

You should receive a structured JSON response containing your quiz questions, choices, and answers:
{ "quiz": [ { "question": "What platform does the workflow use to extract captions from YouTube videos?", "choices": "A. BuildShip\\nB. Apify\\nC. Claude\\nD. OpenAI", "answer": "B" }, { "question": "What AI model is used to generate the quiz questions?", "choices": "A. GPT-4\\nB. DALL-E\\nC. Claude\\nD. Stable Diffusion", "answer": "C" }, // Additional questions... ] }
Troubleshooting Common Issues
If you encounter errors during setup, check these common issues:
Invalid input type: Ensure the Apify node is configured to accept an array, not an object
Model selection errors: For the JSON Generator node, use GPT-4 instead of older models
Missing field names: Double-check your JSON schema to ensure all fields are properly named
Beyond YouTube: Expanding Your Web Scraping Capabilities
While our example focuses on YouTube captions, Apify's ecosystem offers actors for dozens of platforms:
E-commerce data: Extract product information from Amazon, eBay, or Shopify
Social media insights: Gather posts, comments, and engagement metrics from Instagram, TikTok, or LinkedIn
Local business data: Collect business information, reviews, and locations from Google Maps
Each of these can be integrated with BuildShip to create powerful automation workflows. For a step by step video tutorial on this please click below:
Advanced Apify Features to Explore
To get even more from your Apify + BuildShip integration:
Proxy management: Configure proxies through Apify to avoid rate limiting and IP blocks
Storage options: Use Apify's dataset storage for large scraping jobs
Actor marketplace: Explore pre-built actors for specialized scraping tasks
Custom actors: Develop your own actors for unique data extraction needs
Conclusion
Web scraping is no longer just a developer’s tool - it’s becoming a core part of modern automation stacks. With platforms like Apify providing powerful, ready-made scrapers and BuildShip turning complex workflows into visual building blocks, anyone can unlock and repurpose data across the web.
Whether you’re generating quizzes from video captions, pulling reviews from Google Maps, or transforming social media content into insights, the combo of Apify and BuildShip puts that power in your hands. This example is just one piece of what’s possible. Now that you’ve seen how quickly you can go from raw data to a usable API, imagine what else you could automate.