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3 Comments

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.

posted to Icon for group Freelancers
Freelancers
on May 21, 2026
  1. 1

    Came at this from a consumer angle (AI reading app) but same wall: dumping raw book excerpts into Claude blew up both the bill and the hallucinations.
    What worked was extracting a structured JSON summary once at ingest (characters, themes, key passages with offsets), caching it in a separate table, and feeding the model that JSON + a small sampled excerpt — never the raw 500-page text. Token cost dropped to something sustainable, and the model stayed in character across 10+ messages instead of breaking into "as an AI" meta-talk. Different domain than B2B leads, but the underlying point lines up: the cleanup has to happen BEFORE the LLM call, not inside the prompt.
    Caveat — I'm a non-engineer founder, AI-paired everything. Sharing the pattern from what shipped, not a recommendation on the "right" way to engineer it.

  2. 1

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  3. 1

    This is a strong point, but I’d sharpen the positioning beyond “scraping + cleanup.” The real pain is that founders are treating messy web data like it is ready for AI, when the useful layer is actually signal extraction before the model ever sees it. That is where the cost savings, hallucination reduction, and lead quality all come from.

    If you build this into a product or serious service, the naming/category frame matters a lot. “Data Density” and “Real-Time Signal Filtering” are much stronger than generic scraping because they sound closer to AI pipeline infrastructure, not freelance data work. That difference affects whether founders see this as a cheap scraper or a system that improves outbound accuracy and LLM efficiency.

    A name like Exirra .com would fit the broader direction better if you want this to become an AI data-quality and signal-intelligence layer. It sounds more serious than a scraper/service brand, and this category needs trust before founders hand over sales data or plug it into their agent workflows.

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