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.
Completely agree on the data density point — this is one of the most underrated issues in AI pipelines right now. Raw HTML dumps are particularly brutal: a single scraped page can contain navigation, footers, ads, and boilerplate that has zero signal value but eats 60-70% of your tokens. The cleaning layer before the LLM isn't optional, it's load-bearing. One pattern that's worked well in my experience: extract only structured fields at scrape time (no full HTML), then run a lightweight regex/rule pass to strip noise before the LLM ever sees it. The 40% token efficiency gain you mention is actually conservative — with strict pre-filtering I've seen it reach 60%+ on messy e-commerce or news sources. What parser stack are you using for the enrichment layer? Curious whether you're doing this with custom Python or something like Trafilatura/Readability.
This is exactly where the math stops making sense for a lot of people. You’re spot on treating the cleaning layer as optional is basically throwing money away.
I’ve been leaning towards a hybrid approach: using custom Python parsers for the structural extraction to keep the logic tight, then leveraging Trafilatura where the layout is too chaotic to map manually. For high-volume pipelines, even stripping boilerplate via a quick regex pass before the LLM hit makes a massive difference in latency, not just token cost.
The 60%+ efficiency gain is spot on. If you're building out that enrichment layer, are you handling the concurrency at the actor level or pushing to a queue to manage the parser overhead? Would love to hear how you're balancing the scrape-time speed vs. the cleaning compute.