How to Build a Generic AI-Based Web Scraping Solution: A Step-by-Step Guide

Learn how to build a smart, adaptable AI web scraping solution.

With the evolution of technology today and a continued rise in the graph of digital media usage, creatives from across industries, including businesses, researchers, etc., are looking for different ways to collect consumer information to better their products and services.

Now, one of the fastest ways to collect such data is through the smart process of web scraping, where you use an advanced scraper to gather information. This could be in the form of reviews, feedback, social media posts, etc. However, some of the traditional scrapers have limitations to specific websites only, and this in turn becomes a problem if any website changes come across. If there is any minor change in site layout or other aspects of the website, the scraper may avoid scraping data from it, making it difficult to scrape data.

And this is exactly where an AI-based web scraper becomes the best solution. Compared to traditional scrapers, AI-powered new-gen scrapers come with the advantage of scraping data from any and every website. These AI-powered scrapers are not only smarter than traditional scrapers but also more versatile. So, if you are curious about the working of an AI-based web scraper or are planning to build one for your business, this blog is going to give you a practical overview of the same!

What Exactly Is AI-based Web Scraping?

AI-based web scraping is a method of web scraping that effectively automates processes such as data extraction through the use of artificial intelligence.

Now, traditional web scraping solutions come with their own set of limitations across websites. For instance, if the website changes its design, the scraper usually breaks and needs to be fixed. This is where the challenge lies. There are thousands of websites out there, each with its own design and structure. Creating a new scraper for every single one is not practical.

A generic AI-based web scraping solution, on the other hand, solves this problem. It learns to understand different websites on its own. Instead of looking for one fixed layout, it identifies patterns like titles, prices, or names, regardless of where they appear. This makes it useful across many websites without constant updates.

Steps to Build a Generic AI-Based Web Scraping Solution

The steps below will give you an overview of what goes into building a generic AI-based web scraping solution:

Set Your Goal

You must be certain of the type of data you wish to gather before you begin to build a web scraping solution. Do you want job postings, news headlines, product prices, or customer reviews, for instance? You have to give the scraper a clear understanding and an objective that will help it know what to search for. Consider this as an example of handing over a shopping list and asking the person to buy the exact same items as those mentioned in the list. Now, this step holds the utmost importance because it gives you web scraper purpose and clarity. If this information is not aligned in the first step, the tendency of the scraper to gather wrong or unwanted information may increase.

scraping_goal = { 

    "task": "Scrape product details", 

    "fields": ["title", "price", "rating", "image_url"]  

} 

Gather Sample Websites

Once your goal is clear, you should collect a few websites that have the type of data you need. These websites will be used to train the scraper. Make sure you pick different kinds of sites, for example, one with a simple layout and one with a complex layout. This will help your scraper become smart and flexible. You don’t need many websites to start, just 3 to 5 good examples are enough. If you’re collecting product details, choose sites like Amazon, Flipkart, and BestBuy. These sample websites will show the AI how different websites present similar types of information.

sample_sites = [ 

“https://www.amazon.in/”, 

“https://www.flipkart.com/”, 

“https://www.bestbuy.com/” 

]  

Break Pages into Sections

Web pages are made up of different blocks or sections like headings, prices, reviews, images, etc. This is where you will need to technically teach your scraper to recognise these blocks. Do this by labelling examples manually at first - for example, mark the product name, price, and image on a page. This will help the AI learn what is important and will help the AI further avoid the information that is not required. Once the scraper learns what a "price section" or "review block" looks like, it can find the same thing on other websites too.

from bs4 import BeautifulSoup 

import requests 

 

url = "https://www.example.com/product-page" 

 

response = requests.get(url) 

soup = BeautifulSoup(response.content, 'html.parser') 

 

# Manual labelling logic 

product = { 

    "title": soup.find('h1', class_='product-title').text.strip(), 

    "price": soup.find('span', class_='price').text.strip(), 

    "rating": soup.find('div', class_='rating').text.strip() 

} 

Use AI to Spot Patterns

Now comes the smart part. You can train the AI using tools like machine learning models (such as BERT or spaCy) to identify patterns. For example, the AI can learn that prices usually follow a currency sign (₹, $, etc.) and reviews often come after the word “Review” or “Rating.” Over time, the AI starts understanding how data is structured, even on new websites. You don’t have to write new code every time. The AI becomes smart enough to “guess” what each section means. This makes your scraper flexible and reusable across different websites.

import spacy 

 

nlp = spacy.load("en_core_web_sm") 

doc = nlp("Apple iPhone 13 is priced at $799 with a 4.5-star rating.") 

 

for ent in doc.ents: 

    print(ent.text, ent.label_) 

Test and Improve the Scraper

Once the scraper is practically working, it is important to test it on new websites. This will show you how smart and flexible it is. You can track what kind of errors it makes - for example, maybe it missed a title or grabbed the wrong price. Then retrain the AI with better examples. It is just like how people improve with practice.

def extract_title(soup): 

    try: 

        return soup.find('h1', class_='product-title').text 

    except Exception as e: 

        print(f"Error: {e}") 

        return None 

 

# Test and evaluate 

title = extract_title(soup) 

if no title: 

    print("Failed to extract title")  

Add Error Handling

Error handling should be incorporated into your web scraper so that your scraper won't crash in the event of an issue, such as a broken link or a missing title. Rather, it will either try again or bypass the error. Try and except blocks are frequently used in Python for this. Your scraper becomes more professional and stable as a result.

try: 

price = soup.find('span', class_='price').text 

 

Except: 

price = 'Not found'  

Organise and Save the Data

Post the data collection process has been carried out, the data needs to be further cleaned and structured. In terms of structuring, it can be formatted in the form of a CSV or an Excel file, among other formatting options. This is called data formatting. Your data may be difficult to use and analyze if you skip this step.

import pandas as pd 

 

data = [ 

    {"title": "iPhone 13", "price": "$799", "rating": "4.5"}, 

    {"title": "Galaxy S21", "price": "$699", "rating": "4.3"} 

] 

 

df = pd.DataFrame(data) 

df.to_csv('scraped_data.csv', index=False) 

It is indeed a fact that, at first, developing an AI-based web scraper may seem technical. However, when the process is practically divided into minor steps, the process becomes easier to implement. By following the right approach, a versatile and smart AI-based web scraper can be created with the utmost efficiency.

Benefits of an AI-Based Web Scraping Solution

There are several advantages to using AI for web scraping. They include, but are not limited to:

Adaptability

AI scrapers don’t rely on fixed website structures. They recognise patterns and adjust to changes in layout or content automatically, making them much more reliable than traditional scrapers when sites update.

Fewer Errors

AI scrapers improve over time by learning from incorrect outputs. With regular improvements and automated processes, AI-based web scraping solutions tend to make fewer errors as compared to traditional ones.

Scalability

Scalability is one of the biggest benefits of an AI-based web scraping solution. Now, the traditional web scrapers may be limited to certain websites. While, on the other hand, the ones that are backed by AI have the capacity to scrape multiple websites all at once. Plus, the volume can be increased as per your requirements, which in turn helps in scalability!

Less Manual Work

Artificial intelligence has given the human race the benefit of automating tasks at its best. Manual intervention is no longer necessary when tasks are automated. This consequently lessens the need for someone to supervise the web scraping procedure, allowing companies to effectively use their employees to make other business-related decisions while saving time and money!

Future-Ready

As technology advances day by day, bringing in changes to a lot of aspects in the digital world, AI-based scraping solutions in such cases stay updated and relevant. This, in turn, helps businesses track data in real-time, without the need to feed in updates regularly.

Key Use Cases

Undoubtedly, AI-based web scraping solutions aren’t limited to anything. It is completely versatile and has the ability to work a wide range of tasks quickly and efficiently, at all times. To give you an overview of how well AI-based web scraping solutions can be of of use to you, below we are listing a few important use cases:

Price Monitoring

One of the most interesting use cases of AI-based web scraping is price monitoring. Now, as a business, when you deploy this scraping solution, you will be amazed to see how efficiently the scraper can give you insights into prices that your competitors are staying ahead of. The scraper will extract data from all your competitors' websites and help you stay ahead of the curve by providing a detailed understanding of price margins, what is working, and what you should avoid, etc.

Customer Sentiment Analysis

If you are a business that offers any products or services, you will definitely understand how important it is to understand customer sentiments. And this is where AI-based web scraping solutions can help! AI scrapers can quickly extract data around consumer sentiments through thousands of reviews, discussion platforms, social media, etc., giving you better insights to stay ahead!

Competitor Monitoring

Another use case of AI-based web scraping solution is competitor monitoring where you can constantly monitor what your competitors are doing. Moreover, you can also keep a track of what your competitors are launching in the market, their growth factors, etc., among several other aspects!

In Essence,

AI is slowly changing the world for the good and we can firsthand experience it in the space of how we collect the data with ease. That being said, you need to understand that developing and running an AI-based web scraping solution is completely legal. Now this depends on how legally you run the process. We would ideally recommend you to follow the legal user policies stated by the websites to avoid any legal consequences.

And now, if you are planning to deploy an AI-based web scraping solution for your business but do not have the time to build one yourself, then it is ideal to leave it in the hands of a professional like Scraping Intelligence. Here, at Scraping Intelligence, we have been providing AI-powered web scraping solutions across industries. With extensive experience in the industry, we have been serving industries including but not limited to real estate, e-commerce, travel, OTT media platforms, and more! We do not only provide accurate scraping solutions but also deliver data that is completely reliable and consistent! You can rest assured that our web scraping solutions are completely ethically and we only extract data that is publicly available to avoid any legal consequences for all our clients!

Contact us today to schedule a consultation and learn more about our expert services in detail!

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