Stop feeding raw scraped data to your LLMs (You're burning API credits)

Hey Hackers,

I’ve been building real-time data pipelines and custom web scrapers for over 3 years now, and if there’s one major mistake I see founders making right now, it’s this: Throwing raw, unfiltered HTML dumps or messy data straight into an LLM context window.

Doing this does two things:

It triggers heavy hallucinations because of the data noise.

It burns massive amounts of tokens, driving your OpenAI/Anthropic bills through the roof.

Lately, I’ve been focusing heavily on Data Density and Real-Time Signal Filtering for high-intent B2B Lead Generation. Instead of traditional batch scraping (which just extracts thousands of dead, messy contacts), I build custom parsers that clean and enrich data at the scraping layer itself before it ever hits an AI pipeline.

The result? A recent test showed a 40% improvement in token efficiency and zero hallucinations because the input data was strictly high-density.

I’m looking to connect with founders who are currently scaling their outbound sales or building data-dependent AI agents.

If you are struggling with messy data dumps, high API costs, or need hyper-targeted B2B leads that actually convert, let’s swap notes! Drop a comment below or feel free to DM me. Happy to look at your current setup and share some insights.