3/17/2025

Scaled HTML Extraction with AI Agent

Let’s Talk About HTML Parsing

Through negotiations with multiple clients handling large-scale scraping—sometimes across thousands or even millions of pages—we've repeatedly encountered a specific request:

💬 "Can Parsera provide only data extraction capabilities while we handle cookies, bot blocking, and other scraping challenges ourselves? We’d like to send you raw HTML, and you’d handle just the parsing."

📞 The answer is yes!

Many companies do prefer to extract raw HTML on their own for several reasons:

They use their own infrastructure to handle cookies, bot detection, and other anti-scraping measures.
By managing these processes internally, they maintain greater control over the entire scraping pipeline while optimizing costs.

📦 We want to share on of our recent use cases of API HTML parsing, so you that if you find yourself there you’d know who to go to ;

Use Case: Automating HTML Parsing for a Market Intelligence Company

Background

A leading Market Intelligence company collects data from thousands of sources to track industry trends, competitor movements, and emerging market opportunities. They already have a well-established scraping pipeline, handling:

Proxy management to bypass geo-restrictions and anti-bot measures.
Session handling with cookies and headers for authentication.
HTML extraction at scale from various websites.

💡 Despite this setup, one major bottleneck remains—manual coding for data extraction. Their team spends a significant amount of time writing and maintaining custom scripts for each new source, slowing down operations and increasing technical overhead.

The Challenge

Every time they onboard a new data source, their developers need to:

Manually write custom parsing scripts to extract relevant data fields.
Constantly update code when websites change structure.
Allocate engineering resources to maintain existing scrapers instead of focusing on innovation.

💡 This makes the entire process slow, expensive, and inefficient.

How Parsera Solves This

Instead of manually coding parsers, the company integrates Parsera’s LLM scraping agent, which:

✅ Automatically writes parsing scripts tailored to each website.

✅ Self-adjusts when websites change structure, reducing maintenance work.

✅ Seamlessly integrates into their existing pipeline via API

💡 Now it works like this: We get HTMLs by API then Parsera extracts structured data by acting solely as a parser. As a result we allow our clients to have more control over data retrieving and following data management.

The Business Impact

By replacing manual coding with Parsera LLM Scraping Agents , the company achieves:

📈 Faster time-to-market—new data sources onboarded in minutes, not weeks.

💰 Lower operational costs—no need for dedicated engineers to maintain scrapers.

🔄 Scalability—easily expand to hundreds of new websites without coding bottlenecks.