If your AI pipeline is eating 70% of your tokens on navigation footers and ads, you're not scaling - you're leaking cash.

Most teams treat data cleaning as an afterthought. They just dump raw HTML into the context window and pray for good output.

I’ve been building custom pipelines that strip the "noise" at the source before the LLM even sees it.

The Result: 60%+ token efficiency and higher conversion rates.

The Workflow: I’m using a mix of structured extraction and rule-based filtering that keeps the signal-to-noise ratio high.

Building stable data-enrichment pipelines is a grind, especially when dealing with chaotic scraping environments.

Are you building a data-heavy AI product? Let’s talk about how you’re managing your context window costs. I’m looking to trade notes on cleaning stacks.

#indiehackers #buildinpublic #webscraping #saas #ai #datacollection #automation #techfounders