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.
Really on point, and it hits close to home — I'm building a tool that analyzes patents and scientific literature with an LLM, and the risk of dumping raw unfiltered data in is very real, both for the noise it introduces into responses and for costs climbing for no good reason. I'm curious about your approach of cleaning/enriching data at the scraping layer instead of leaving all that work to the AI afterward. One question: for very heterogeneous data (e.g. patent text + academic abstracts with different formats), do you customize the parser per source, or have you found a way to generalize the cleaning without rebuilding it from scratch each time?
This is exactly the kind of hidden cost most teams don’t notice until their AI stack gets expensive and unreliable.
The real insight here is not just “clean data” — it’s deciding what deserves to enter the context window in the first place, before optimization even begins.
I help SaaS and AI teams tighten this message-to-signal clarity gap in their funnels (async, no calls) when they’re trying to reduce noise and improve conversion + output quality.
Spot on. Most people jump straight to prompt engineering or RAG, but the real bottleneck is almost always pre-context curation. If you aren't filtering before the window, you're just paying to hallucinate faster.
Building a tool to automate exactly that data-tiering right now. Love the focus on async funnels conversion is a byproduct of signal, not volume.