Why not just use Claude?
Why institutional data programs need more than an LLM for reliable, repeatable, verifiable web-scraping operations at scale.
17 pages · By BC Wilson, Chief Operating Officer, Sequentum
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An LLM can turn a plain-English description of a scraping task into a working prototype in minutes. But the same prompt against the same page can return a different answer on the next run. When we asked an LLM to extract one live product page three times, it gave two different answers.
For data that feeds a trading model, a pricing engine, or a customer's data feed, that is disqualifying. The paper argues for a different split: use the LLM to build the agent, then run it on a deterministic engine that can be versioned, tested, and audited.
What's inside
- Why LLM output changes from run to run, and why prompting can't fix it
- How anti-bot defenses flag live LLM browsing sessions
- What is at stake for banks, retailers, and data resellers
- The architecture that works: AI-assisted build, deterministic run
“A well-engineered scraping program answers the question ‘what is on this page, according to this fixed rule?’ every single time. A live LLM call answers the question ‘what does the model currently believe is on this page?’”
Read the full white paper
Illustrative use cases for an investment bank, a retailer, and a value-added data reseller, plus the risk and compliance considerations for each.