Build high-quality AI training datasets from the web — text corpora, instruction pairs, Q&A datasets, multilingual content, and domain-specific collections. We scrape, clean, deduplicate, filter for quality, and deliver data ready for LLM pre-training, fine-tuning, and RLHF pipelines.
AI training data scraping is the large-scale collection, cleaning, and structuring of web content to create datasets for training and fine-tuning machine learning models. Modern large language models are trained on trillions of tokens of text data — the quality, diversity, and domain composition of that data directly shapes the model's capabilities, biases, and failure modes. Building this data infrastructure is as technically demanding as training the model itself, and it is where many AI projects underinvest.
Web-sourced training data is not simply raw HTML dumped into a file. Useful training data requires an extensive processing pipeline: content extraction that strips navigation, ads, and boilerplate from meaningful article text; language identification and filtering; quality scoring to remove low-information content; near-duplicate detection and removal at scale; personally identifiable information (PII) filtering; domain and topic classification; and format standardisation into training-compatible schemas like JSONL. DataFlirt handles this entire pipeline, not just the raw collection.
Different training objectives require different data types. Pre-training large language models requires broad, high-quality text corpora across domains and languages. Instruction fine-tuning requires structured instruction-response pairs demonstrating desired model behaviour. RLHF (Reinforcement Learning from Human Feedback) requires preference data comparing model outputs. Retrieval-augmented generation (RAG) requires structured, factually accurate documents in specific domains. DataFlirt can build datasets for each of these use cases — to the specifications your training pipeline requires.
Domain specificity is a growing focus for AI teams. General-purpose web corpora are widely available, but high-quality domain-specific datasets — financial filings and news, legal judgments and legislation, medical literature, technical documentation, Indian-language content — are scarce and commercially valuable. DataFlirt's deep scraping coverage across these domains, combined with domain-expert quality review, makes us a specialist partner for teams building domain-adapted models.
Comprehensive extraction built for reliability, accuracy, and scale.
High-throughput scraping of news, blogs, forums, documentation, academic papers, and domain-specific web content at billion-token scale.
Boilerplate removal, HTML stripping, encoding normalisation, and content quality filtering to extract meaningful text from raw web pages.
Exact and near-duplicate detection using MinHash and SimHash algorithms across billion-document collections to eliminate training data contamination.
Language-identified collection across 150+ languages with specialised pipelines for Indic scripts — Hindi, Tamil, Telugu, Bengali, Kannada, and more.
Automated detection and removal of personally identifiable information, toxic content, and training data that violates responsible AI guidelines.
Output in JSONL, Parquet, HuggingFace Datasets format, or custom schema. Full metadata: source URL, scrape date, language, domain, quality score.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "status": "success", "dataset_id": "df_corpus_en_web_0019", "language": "en", "domain": "financial_news", "documents": 2480000, "tokens_est": 4200000000, "quality_score":0.94, "dedup_rate": "98.2%", "pii_filtered": true, "format": "jsonl", "delivery": "s3://your-bucket/corpus/" }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
Distributed async crawlers capable of collecting and processing hundreds of millions of pages per month for web-scale dataset construction.
trafilatura and custom extractors remove navigation, ads, comments, and boilerplate — preserving only meaningful body text for training.
datasketch MinHash LSH deduplication identifies and removes near-duplicate documents across billion-document collections efficiently.
Specialised extraction and quality filtering for Devanagari, Tamil, Telugu, Bengali, and other Indic scripts with language-native tokenisation.
Microsoft Presidio-based PII detection identifies and redacts personal information across collected text before dataset packaging.
Perplexity-based and heuristic quality scores filter out low-information content — spam, auto-generated text, and SEO boilerplate.
From solo analysts to enterprise data teams — here's how organizations use this data.
The most capable AI models are built on the most carefully constructed training datasets. Raw web dumps, poor deduplication, and unfiltered content produce models that hallucinate, repeat, and fail on domain tasks. DataFlirt approaches AI training data as a precision engineering problem — with the same rigour applied to collection, cleaning, and quality filtering as you apply to model architecture and training code.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
