The best article extractor around. Hands down.
Clean title, author, date, and body from any publisher on earth. Six years of tuning, JavaScript rendering and anti-bot built in, and output that is genuinely model-ready — all in one API call.
The article extraction challenge.
AI applications need clean, structured text. Raw web pages are 90% noise — and noise degrades model performance and pollutes training data.
Ads, navs, sidebars, cookie banners. Drag them into embeddings and your retrieval quality collapses.
Every publisher formats articles differently. Reliable extraction across thousands of sources is the hard part nobody tells you about.
Menus, footers, "related articles", share widgets. All polluting the body text you actually want.
Custom extraction scripts break the day a site redesigns. The maintenance burden eats your roadmap.
What AI teams build with it.
Feed your retrieval. Skip the cleanup.
Pipe clean, structured article content straight into your vector store. Embeddings capture meaning, not boilerplate. Metadata (date, author, topic) enables filtering and relevance ranking.
- RAG answer accuracy lifts when embeddings stop encoding nav bars
- 10,000+ articles/day ingested automatically
- ~90% reduction in content preprocessing time
- Metadata-based filtering for more relevant retrieval
Build training corpora from the open web.
Extract clean text with consistent formatting across hundreds of sources. Build domain-specific corpora for fine-tuning. Markdown mode preserves headings, lists, and emphasis so models learn formatting too.
- Domain-specific training corpora from thousands of sources
- Better model performance via clean, well-structured data
- Dataset prep cut from weeks to hours
- Consistent text quality across diverse source formats
Power topic-aware news feeds.
Collect, categorise, and summarise articles from hundreds of sources. AI-powered topic classification, sentiment analysis, and trend detection on consistently formatted data. Power automated briefings, alerts, and dashboards.
- 10,000+ articles aggregated and categorised daily
- Breaking trends detected ~80% faster
- Auto-generated summaries and briefings
- Personalised feeds via topic + sentiment analysis
Keep the chatbot KB current, automatically.
Ingest help-center articles, docs, blog posts, FAQs to feed conversational AI. Clean extraction means the chatbot retrieves relevant answers without HTML artifacts. Auto-tagging routes questions to the right content.
- Higher chatbot answer accuracy
- KB stays current via automated ingestion
- ~85% reduction in manual KB maintenance
- Source attribution via metadata
Extract from every publisher format.
One API handles every layout — no custom parsers, no per-site maintenance.
Reuters, AP, Bloomberg, and thousands more. Clean text, headlines, authors, dates across any layout.
Medium, Dev.to, Hashnode, and tens of thousands of tech blogs. Diverse layouts + JS-rendered content handled.
Articles + papers from research repositories. Structured content with abstracts, citations, author info for academic AI.
Company blogs, press rooms, product announcements. Thought leadership and industry analysis for competitive intel.
Policy docs, press releases, regulatory updates. Build compliance + policy-analysis AI on official sources.
Newsletter content from Substack and similar platforms. Author insights, analysis, commentary for content curation.
Enhance feed items with full-text extraction. Beyond truncated summaries — complete text + metadata.
Research papers, abstracts, supplementary materials. Build AI training datasets from peer-reviewed content.
From URL to AI-ready content in 3 steps.
Send article URLs
Pass any URL to the Extract API or Markdown API. JS rendering and anti-bot are automatic. For site-wide ingestion, feed us your URL list from a sitemap and submit as an async batch. Article boundaries are detected regardless of publisher CMS.
Automatic content isolation
Ads, navs, sidebars, footers, cookie banners, related-articles widgets — all stripped. Multi-page articles auto-merged. Output is clean, readable content ready for AI.
Receive structured output
Title, author, date, language, images, article-confidence score. Choose plain text for embeddings, markdown for LLM prompts, or cleaned HTML for display.
Drop an article URL in.
See clean extraction in real time — title, author, date, body, metadata.
curl 'https://api.ujeebu.com/article' \
-H 'ApiKey: YOUR_API_KEY' \
-G \
--data-urlencode 'url=https://www.theverge.com/tech/935898/asus-rog-zephyrus-g14-2026-intel-nvidia-review' \
--data-urlencode 'summary=true' \
--data-urlencode 'html=false'Built for production AI pipelines.
Clean text extraction
Pure article text with ads, navs, sidebars, boilerplate removed. Output ready for embeddings, summarisation, or LLM prompts without preprocessing.
Automatic metadata
Title, author, publication date, language, site name, hero image — parsed from Open Graph, JSON-LD, HTML meta. Use for filtering, dedup, and organisation.
Markdown for LLMs
LLM-optimised markdown via the Markdown API. Preserves headings, lists, emphasis, and links — formats models understand natively.
100+ languages
Multi-byte characters, RTL text, complex scripts handled. Language detection tags each article for multilingual training and filtering.
Pagination handling
Multi-page articles auto-detected and merged. Up to 30 pages combined seamlessly. No more truncated articles in your dataset.
Batch processing
Feed sitemap-derived URL lists into async batch jobs. Thousands of articles extracted in parallel with rate limiting and retry baked in.
Frequently asked.
How clean is the extracted text vs raw HTML scraping?
Can I extract from paywalled or login-protected sites?
How does it handle multi-page articles?
How does this compare to newspaper3k / readability?
Can I get markdown output for LLM prompts?
Rate limits and capacity?
Start extracting articles for AI today.
Join AI teams building RAG pipelines, training datasets, and content-intelligence systems on clean article extraction.