Is AI an Effective Product Data Extractor?

By MyQuants | July 2025

As businesses increasingly rely on product data for pricing strategies, inventory management, and market analysis, the question arises: can AI, particularly large language models (LLMs), reliably extract product information from websites without the need for devs? Let’s examine how AI stacks up against traditional scraping methods and the newer KVCI using the catalogue page of an e-commerce site containing 59 products.

Using an LLM API to Directly Extract Data

Firstly, we tested using LLMs to directly extract each product from raw HTML and return a list of dictionaries containing product description, url, image_url and price. The page tested contained 59 products, of which 3 of the 5 models used successfully returned the correct data, with an average return time of 8 seconds. There are a few key findings:

• Input Token Count: Using raw HTML, even with scripts and styles filtered out, results in massive token counts, the tested page was not particularly complex by e-commerce standards but still managed to be 188,740 input tokens
• Cost Efficiency: The tested product page cost about $0.08 using GPT-4.1 Nano , $0.06 using Gemini Flash. Larger pages quickly exceed input limits, making it impractical for high-volume scraping.
• Speed: The LLMs took roughly 8 seconds per page for extraction.
• Limitations: The lower cost and smaller LLMs failed to capture all data, while larger LLMs had great success but increased costs significantly.

In short, AI can achieve very accurate results but at high cost and with speed limitations.

Extraction Using Traditional Tools (Python and BeautifulSoup/BS4)

Extracting product data without AI, using libraries like BeautifulSoup (BS4), offers a dramatic cost and speed advantage but requires a new scraper to be built for each website:

• Accuracy: All 59 products were extracted correctly in testing.
• Performance: Processing the product page took ~0.1s
• Cost: Using AWS Lambda at $0.0000000083 per millisecond, extraction costs around $0.0000008 per page.
• Limitations: Every website requires a custom scraper; any change in layout may break the script.

Traditional scraping is extremely cheap and fast, but scaling requires constant maintenance.

Using KVCI

The KVCI methodology combines speed, accuracy, and cost efficiency:

• Accuracy: All 59 products were extracted correctly in testing.
• Speed: Processing the product page took ~0.3s
• Cost Efficiency: At $0.0000024 per page.
• Limitations:3x Slower than raw BS4, but time is saved in maintenance and scale.

KVCI approach achieves similar generic scraper accuracy to larger AI models but at a fraction of the cost and without input context limitations.

Using an LLM to Write BS4 Scrapers

Could we combine the real Power of LLMs with the speed of custom BS4 scrapers? Is It possible to use AI to generate BeautifulSoup scripts automatically from a single prompt? This would eliminate the developer overhead. Unfortunately in testing, all 5 models incorrectly built BS4 scrapers in the first 4 prompts (main errors included incorrect class identification and missing data like descriptions). Meaning the resulting scrapers still require validation, debugging, and maintenance for each website. So the costs of the legacy techniques are not fully avoided. However, it does make building individual scrapers much, much faster and is extremely cost efficient when compared to the price of a junior dev assisting in maintenance.

Comparison of Product Data Extraction Methods

Conclusion

AI can be used as a product data extractor, but cost, speed, and context limitations make it impractical for high-volume data extraction. Traditional scraping is cheap and fast but brittle. KVCI provides a balanced solution, offering high accuracy, scalability, and extremely low costs, making it one of the most effective product data extraction methods available today.