Why Did We Build an AI Agent for Large Scale Scraping?

💭 Why Did We Build an AI Agent for Large Scale Scraping?

Parsera started with a 'URL extractor' that processes every page you request using an LLM (Large Language Model) or simply AI to extract data—and it worked more than perfectly.

But then, by analyzing the market and meeting more and more clients we realized that the primary need for parsing & scraping — and its relevance — is most evident when dealing with large volumes of data, both in terms of content (pages) and sources (websites).

Through discussions with various clients, two main use cases emerged:

📌 Businesses need to collect large amounts of information from a single source with recurring data structures. For example, extracting hundreds of pages from an e-commerce site, including both catalog and product pages.

📌 Monitoring multiple different information sources for updates by tracking changes on the same page multiple times. For example, this is particularly useful when you need to keep a pulse on your competitors’ pricing or track new tenders in your industry to be the first to apply.

🤖 This led us to develop our own Agent. Here’s how it works:

1. The agent accesses an endpoint, extracts the data structure, and generates a script (scraper code) to retrieve the required information.
2. This code is then used for subsequent scrapes on similar pages.
3. As a result, when you request data, the agent generates a scraper that extracts data without needing to parse each page repeatedly—since it is done through the generated code.

Benefits for Businesses that need Large Scale Scraping

🔧 The Agent is a tool that automatically codes scrapers, so even if a scraper breaks, the agent can regenerate it.

⚙️ Another key advantage, crucial for sustainable large-scale scraping across thousands of pages, is that the generated code eliminates AI hallucinations — when AI or Large Language Models (LLMs) produce nonsensical results.

🏎️ We no longer run a slow and expensive LLM for every scrape because we use code. For instance, in the 'Monitoring' use case, everything now works several times faster, and you won’t go broke paying for an LLM on every request.

💡 All of this makes Large Scale Scraping (or parsing, as it’s sometimes called) feasible, fast, and reliable.

🎁 We welcome you to try it by yourself HERE