These days, everyone asks like this.
"Do we really need to do web crawling when we have GPT and Google search?"
This question may seem reasonable, but those who have actually tried automation and data analysis nod their heads vigorously.
1. Search is for 'humans', crawling is for 'machines'
Searching is convenient for humans. When you enter the necessary words, various results are displayed. However, there are significant limitations here.
- Not automatable
- Not traceable
- Not analyzable
Why is that?
It's because search provides "results that are easy for humans to read." On the other hand, crawling creates "data that machines can handle." The purposes are different.
2. Areas where search absolutely doesn't work
- Content that requires login to view (e.g., community posts, internal systems)
- Reviews or comments loaded with infinite scroll or Ajax
- Shopping mall information with slightly different structures on each page
- Price information that keeps changing over time
Search engines cannot index or require manual checking for these types of data. GPT also cannot scrape such data.
3. Crawling allows for 'structured repetitive tasks'
For example, let's say you want to collect data from a shopping mall based on the following criteria.
"Retrieve 1,000 products with ratings above 4.5 priced under 100,000 won."
This is not possible with search or GPT. However, it is achievable through crawling.
You can automatically explore thousands of product pages, extract only the desired information that meets the criteria, and store it in a database.
And if you automate this task to repeat every day at 2 a.m.? → Complete automation, the beginning of data-driven decision-making.
4. Paradox of 'In the AI era, humans are busier'
There is a trend in the translation industry these days. Even if translations are done using GPT, humans still need to read and review them. That's why translation companies have become even busier.
"We are busier because AI translates."
Ironical, isn't it? However, this is not just about translation. The same goes for data.
5. Trust in data comes from 'well-designed collection'
Results scraped by GPT, articles found through search. Even if they look plausible, to actually use them, you need to check the following:
- Is the information up to date?
- Does it meet our desired criteria?
- Does it include all the necessary data?
There is only one way to confirm and control this. Using a well-designed crawler created by humans.
6. Conclusion: Search is the 'eyes', crawling is the 'hands', GPT is the 'brain'
No matter how smart GPT is, someone needs to manually fetch accurate data.
- Search allows viewing and reading but lacks structuring.
- Crawling accurately fetches the desired information.
- GPT excels in summarizing, analyzing, and utilizing that data.
Search = Eyes
Crawling = Hands
GPT = Brain
When these three are combined, true automation and insights begin.
Hashscraper starts here.
We scrape data faster than anyone, structure it in a way that is easy for humans to use, and provide it for GPT or LLM to use immediately.
Data collection, automation, AI utilization. All of this begins with 'accurate collection'.
Email: help@hashscraper.com
Phone: 02-6952-1804
