If your AI pipeline is eating 70% of your tokens on navigation footers and ads, you're not scaling - you're leaking cash.
Most teams treat data cleaning as an afterthought. They just dump raw HTML into the context window and pray for good output.
I’ve been building custom pipelines that strip the "noise" at the source before the LLM even sees it.
The Result: 60%+ token efficiency and higher conversion rates.
The Workflow: I’m using a mix of structured extraction and rule-based filtering that keeps the signal-to-noise ratio high.
Building stable data-enrichment pipelines is a grind, especially when dealing with chaotic scraping environments.
Are you building a data-heavy AI product? Let’s talk about how you’re managing your context window costs. I’m looking to trade notes on cleaning stacks.
#indiehackers #buildinpublic #webscraping #saas #ai #datacollection #automation #techfounders
This is a very real leak.
A lot of teams optimize the model choice first, but the bigger cost driver is often what enters the context window in the first place. If navigation, duplicated blocks, ads, and raw HTML all get passed through, even a cheap model starts looking expensive.
Your structured extraction + rule-based filtering approach makes sense. I’d probably think about it as two layers: first reduce noise before the LLM sees it, then route the cleaned workload to the right model depending on whether the task is tagging, summarizing, ranking, or deeper reasoning.
That second layer is what we’re working on at EvoLink Model Router: unified model access, routing, and cost control for AI workflows as volume grows.
Curious how you measure the cutoff point: when does filtering start hurting answer quality instead of saving tokens?
That is a great perspective. The 'upstream' noise reduction is definitely the most effective lever for cost control once the model sees the noise, you’ve already paid for the tokens.
The routing pattern you mentioned for different workloads (tagging vs. reasoning) is exactly how I’m thinking about scaling this. As for measuring the 'cutoff point' where filtering starts hurting quality, I’ve been using a two-pronged evaluation approach:
Semantic Delta Analysis: I run a small validation set where I compare the outputs from 'raw-context' vs. 'filtered-context' runs. If the semantic similarity score (using embeddings) drops below a certain threshold (e.g., 0.90), it indicates that the filtering layer is stripping away too much nuance or 'latent' context that the model needs for reasoning.
Task-Specific Benchmarking: For 'tagging' and 'summarization,' the cutoff is usually very aggressive because these tasks are brittle noise is rarely helpful. For 'deeper reasoning,' I maintain a higher retention threshold, often preserving secondary structural elements that might seem like 'noise' but actually contain implicit metadata.
It’s definitely a moving target. I’d be curious to know how EvoLink handles routing decisions are you using a pre-router LLM call for task classification, or a statistical approach based on payload characteristics?