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.
The cleanup-vs-cost tradeoff is real, but what I never see anyone benchmark publicly is which preprocessing actually moves the needle on output quality vs which just trims tokens. Are you finding chunk-aware dedup matters more than noise stripping, or the other way around?
This is the billion-token question. In my testing, noise stripping (boilerplate removal) wins on cost and raw latency, but chunk-aware deduplication is the actual quality multiplier for RAG.
If you strip the noise, you get cleaner input, but if you don't dedup at the chunk level, the LLM just gets stuck in "echo chambers" of identical information, which tanks the coherence of the output. I’ve found that high-quality noise stripping is table stakes, but dedup is what actually improves the retrieval relevance.
Are you seeing the quality drop-off happen more on the retrieval side or the context-stuffing side when you run these benchmarks?
“Data density” is probably the most underrated part of AI pipelines right now.
A lot of people assume better prompts solve hallucinations, when the real issue is often garbage context. Once the model has to fight through navigation text, duplicate blocks, boilerplate, and irrelevant markup, you’re already degrading output quality before generation even starts.
Interesting point about cleaning at the scraping layer itself instead of post-processing later. Feels like that becomes even more important as agents move toward real-time workflows instead of batch jobs.
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.
This is pure gold, You basically built a custom semantic metadata layer at ingest which is honestly the gold standard for handling long-form content. Relying on the prompt to filter 500 pages of raw noise is a guaranteed way to go broke and trigger meta-talk.
The JSON caching approach is brilliant, especially for keeping the model 'in character' across longer context windows.
Since you mentioned you’re a non-engineer founder scaling this via AI-pairing, optimizing those ingest pipelines (like automated schema extraction or setting up multi-agent routines to handle the initial parsing without hitting rate limits) can get tricky as your user base grows.
I specialize in backend data automation and agentic infrastructure (frequently building pre-processing and data-cleaning layers to protect the LLM context). If you ever need a technical hand to scale up your ingest pipelines, automate the JSON restructuring, or optimize your database vectors on a flexible contract basis, I'd love to help out. Keep shipping!"
Thanks — and honestly you described the "why" behind the approach better than I did. For now I'm handling everything through AI-pairing and it's holding up at the scale I'm at. Appreciate the offer though, keep building.
Glad the AI-pairing approach is holding up that’s the beauty of building small and scaling only when the bottleneck forces your hand.
Keep it up! Let's stay in touch.
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.
Hey aryan,
Most founders building with LLM agents are feeding them raw web data and wondering why their token bills are skyrocketing and the model is hallucinating over basic data sets.
At Exirra, we solve the 'Ferrari with the handbrake on' problem. We don't just scrape; we build a dedicated Real-Time Signal Filtering and Data Density layer right at ingest. By extracting clean, structured JSON summaries before the data ever hits your LLM context window, we lower token costs and keep your agents strictly in character.
Since you are scaling your AI workflows, let’s grab 10 minutes to see how we can optimize your ingestion pipeline and protect your context window from raw data noise.
One practical thought here.
The pitch you wrote using “At Exirra” already shows the direction is working: it sounds much more like an AI data-quality and signal-intelligence layer than a scraping service.
Since the full domain decision may be early, I can help in a lighter way first.
I do focused naming and positioning audits for early products: category frame, current name risk, domain/name ceiling, buyer perception, and how to make the offer sound like serious AI infrastructure instead of freelance data work.
For your case, the audit would focus on whether this should be positioned as scraping cleanup, signal filtering, data density, AI ingestion infrastructure, or outbound intelligence infrastructure.
Not a long consulting thing. Just a sharp written breakdown you can use before building more landing page copy, outbound, or product assets around the wrong frame.
I’m doing a few of these at $99 while refining the format. If useful, message me privately and I can put together a clear outside read.
Best place to discuss privately:
https://www.linkedin.com/in/aryan-y-0163b0278/
That positioning is exactly the direction I meant.
The fact that “At Exirra” reads naturally in that pitch is the important part. It makes the product sound like an AI data-quality and signal-intelligence layer, not a scraping service.
Just to be clear though, Exirra.com is a domain I control. So if Exirra is only being used here as a positioning example, no issue.
But if you are seriously considering it as the actual product/company name, I would not build the pitch, landing page, or outbound motion around it before securing the .com. That is where the risk starts: the name begins shaping the category, but the company does not control the asset.
If Exirra feels like a real candidate for the product, message me privately and we can see if there is a clean founder-friendly way to make it work.
Really appreciate the transparency and the advice on the asset. You’re spot on positioning it as "signal intelligence" rather than just a "scraper" is the crucial shift in framing for the target audience.
I’m currently validating the core functionality and market fit before locking in the brand identity permanently. I’ve taken note of the .com risk you mentioned it's a solid point on long-term scalability.
Thanks for the offer to chat privately about the naming strategy; I’ll definitely reach out once I’ve ironed out the immediate product milestones. Excited to keep building this out!
That makes sense. If you’re still validating the core product, I would not overcommit to the final brand yet either.
But I would lock the category frame early, because that affects the landing page, outbound, ICP, and what buyers think they’re actually paying for.
For this kind of product, the big difference is whether people read it as:
scraping cleanup
AI ingestion infrastructure
signal filtering
outbound intelligence
data-quality layer for agents
Those are very different buyer expectations.
If useful, drop your email and I’ll send over a tighter outside read. I can map the strongest category frame, buyer angle, and landing-page direction without turning this thread into a long teardown.
That is a spot-on observation. You’re right the category frame isn’t just branding; it defines the entire buyer's mental model and expectations. I've been oscillating between 'AI ingestion infrastructure' and 'data-quality layer for agents,' as those align most closely with the technical value I'm seeing in my early workflows.
I would love to get your outside read on this. My email is [email protected].
I’m particularly interested in your take on whether focusing on the 'data-quality layer' makes it too niche for general-purpose scraping teams, or if that’s actually the strongest hook for buyers currently struggling with unreliable AI outputs. Looking forward to your insights!
Sent you a note by email.
I think the category decision matters more than the naming discussion right now. If you're interested, reply there and we can continue it properly.