Web Scraping · AI Training Data
Your model is only as good as its training data.
We build scraping pipelines that collect, clean, and structure data specifically for ML training, LLM fine-tuning, and RAG knowledge bases.
Most ML teams spend 80% of their time on data, not models. The scraping infrastructure required before training can even begin is consistently underestimated. By the time you've built a reliable collection pipeline, cleaned the data, and structured it correctly, weeks have passed.
We handle that layer: multi-source collection, deduplication, format normalization, multi-language handling, and schema-validated delivery. You get datasets that are ready for your training pipeline on day one, not after a month of preprocessing.
What we collect for
LLM fine-tuning datasets
Domain-specific text corpora. We collect, clean, deduplicate, and format to your training pipeline's spec.
RAG knowledge bases
Company docs, industry sources, product data. Chunked, embedded, and ready for ingestion.
Classification training sets
Labeled examples scraped from structured web sources. We handle the labeling pipeline too.
NLP datasets
Reviews, support tickets, forum posts. Language-tagged, deduped, and schema-validated.
Competitive intelligence datasets
Pricing, features, positioning across competitors. Structured for analysis, not raw HTML.
Our edge
We also build the ML systems that consume the data. We know what clean training data actually looks like from the model's perspective: chunking strategy, deduplication approach, format consistency, metadata tagging. We handle those decisions because we've seen what happens when the data layer is sloppy. Models trained on poorly structured data don't improve, regardless of architecture.
Related case studies
Related services
Need training data? Let's scope the collection pipeline.