Feed your RAG pipeline. Fresh web data, ready to embed.
Turn docs, help centers, and whole websites into clean Markdown for your RAG pipeline, then keep the index current with monitors. No HTML parsers or cleanup scripts to maintain.
1,000 free credits a month · no card required
Sources
crawl
- /billing/payment
- /api/keys
- /guides/sso
- /changelog
help.acme.com · 312 pages
Markdown chunks
main content
- ## Payment methods212 tok
- ## Rotating API keys168 tok
- ## v4.2 release notesnew
Your vector index
your stack
4,812
chunks embedded
monitor: /changelog changed
1 page re-embedded · 2m ago
crawl → markdown → your embeddings
Teams feeding RAG with Context.dev
SiteGPTHyperspell
Agently
SourcelyNotra
Polished.ad
“I installed the SDK, swapped the scraper service behind our Brain, and deleted the polling code the same day.”
- for SiteGPT to move its chatbot knowledge bases
- < 1 day
- for Hyperspell’s web content and company data
- 1 API
Ingest, clean, and keep it fresh.
Crawl a site into Markdown with only the main content, chunk and embed it with your own stack, and let a monitor tell you when a source page changes.
- IngestCrawl a site, map it, or scrape a URL list
- CleanMain-content Markdown, no nav or footers
- RefreshMonitors flag the pages that changed
npm install context.devingest.ts
What teams build on fresh web data.
RAG pipelines our customers run on Context.dev, from support bots to company brains.
Support chatbots
Train each customer’s bot on their own docs and help center, as clean Markdown.
SiteGPTCompany brains
Combine public website content with Slack, email, and Drive in one source of truth.
HyperspellKnowledge at signup
Read a new customer’s website during signup so the agent knows the business.
AgentlyResearch corpora
Crawl journals and PDFs into a corpus a research product can cite.
SourcelyBrand knowledge bases
Feed ad generation with crawl-ready Markdown about each brand.
Polished.adChatbots with live search
Pair a knowledge base with web search and fetch for what it doesn’t know.
Notra
Skip the ingestion code you’d have to babysit.
What a homegrown RAG ingestion pipeline needs before the first embedding, and what Context.dev handles.
| Compare | Homegrown ingestionbuild and maintain | Context.devone API key |
|---|---|---|
| Getting the pages | A crawler, headless browsers, and proxies | Crawl, map, or scrape through one API |
| Clean text | Per-site parsers to strip nav and footers | Main-content Markdown on every page |
| PDFs | A separate document parsing pipeline | Web pages and PDFs through the same API |
| Freshness | Re-crawl everything on a schedule | Monitors flag only the pages that changed |
| Cost | Servers, proxies, and engineering time | 1 credit per page, failed pages are free |
RAG in production, in their words.
All customer storiesRAG pipelines, answered.
More details in the API documentation.
What is a RAG pipeline?
A RAG (retrieval-augmented generation) pipeline ingests documents, splits them into chunks, embeds them, and retrieves the most relevant chunks to ground an LLM’s answer. Context.dev handles ingestion for web content: it crawls sites and returns clean Markdown ready to chunk and embed.
How do I get website data into a RAG pipeline?
Crawl the site with Context.dev to get main-content Markdown for every page. Split each page into chunks, embed them, and store the vectors with the page URL so answers can cite their source.
How do I keep a RAG index up to date?
Create monitors for the pages or sitemaps you index. When a run detects a change, the webhook tells you which page changed, so you re-embed that page instead of re-crawling everything.
Can I ingest PDFs as well as web pages?
Yes. Scrape and Crawl turn PDFs into Markdown alongside web pages, and the Parse API converts PDF and Office files you upload.
Feed your pipeline today.
1,000 free credits every month, enough for 1,000 pages of Markdown. No credit card required.