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Extract, crawl and structure web data at scale with Olostep's Web Data API. Built for AI teams, data pipelines and automation. Fast, reliable and cost-effective.
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Olostep: Web Data Infrastructure for AI
Welcome to Olostep. We're building the infrastructure AI agents need to do real work on the web.
The web is the backbone of every business and the home of humanity's collective knowledge. Humans browsed it manually for decades. Now Agents are knocking on the door.
In late 2025, with improvements in AI models, we entered an era where software stopped following instructions and started thinking. We shifted from an instruction-based world to an intent-based one. The agentic era. That shift breaks a lot of infrastructure that quietly assumed a human in the loop: email, payments, identity, search.
Search asked you to do the rest. You typed a query, got some links back, and then opened the tabs, read, compared, and pieced it together yourself. In an agentic search world, what comes back isn't links, and isn't an answer. It's the work, done: hundreds of sources searched, pages read, fields extracted, conflicts resolved, output structured and delivered.
The web's second user is arriving, at machine scale. Everything it needs to work has to be built again. The data infrastructure is where we start. We are starting with a series of APIs to search, scrape, crawl, map, monitor, and turn the web into clean data for AI
Hey Product Hunt 👋
AI agents are becoming the Web’s second user.
But most agents still access the web like humans do: open tabs, wait for pages, fight JavaScript, parse messy HTML, maintain brittle scrapers, retry failed jobs, and hope the data is fresh.
That felt backwards.
So @hamza_ali59 built Olostep: web infrastructure for AI agents - one API to search, scrape, crawl, map, monitor, and structure live web data for AI products and automation workflows.
What you can do with Olostep:
🔎 Search the live web with natural language queries
🧹 Scrape any URL into clean Markdown, HTML, JSON, screenshots, PDFs, or raw content
🕸️ Crawl entire websites and retrieve LLM-ready pages
🗺️ Map a domain and discover URLs beyond basic sitemaps
⚡ Batch process large URL lists for production data pipelines
📡 Monitor pages, prices, job openings, DOM changes, content updates, and business signals
🤖 Answer questions with web-grounded sources and structured output
🧩 Connect it to your stack with SDKs, CLI, MCP, n8n, LangChain, Mastra, Apify, Zapier, Cursor, Claude, and more
Why Hamza built this:
The bottleneck for AI products is often not the model anymore.
It’s the web layer.
Agents need fresh, structured, reliable data from the real world. Not stale training data. Not one-off browser scripts. Not scrapers that break every time a website changes.
We believe the next generation of AI companies will need a production-grade way for agents to read, reason over, and act on the web.
That is what Olostep is becoming.
Who it is for:
AI teams building agents, copilots, and RAG pipelines
Developers who need clean web data without maintaining scraping infrastructure
Growth, sales, and data teams enriching companies, leads, spreadsheets, and CRMs
SEO / GEO teams monitoring brand visibility across search and AI answers
Recruiting, finance, research, ecommerce, and competitive intelligence teams automating web research
Try this today:
Give Olostep one messy web workflow you currently do manually - for example:
“monitor 50 competitor pricing pages every week”
“crawl these docs and turn them into clean RAG context”
“extract structured product data from a marketplace”
“find companies hiring for a specific role and enrich them”
…and Olostep should help turn that workflow into clean, structured data your app or agent can actually use.
🧪 Try it: Get a free API key
📚 Docs: Read the docs
💬 Community: Join our Slack
🎁 Product Hunt launch offer: They are giving PH builders 80% off for the first month, on any plan! Use code PH80 or comment here after signing up and we’ll help you get set up.
Excited to hear what you think :)
How do you decide when parsers should handle extraction versus letting the model interpret the data?
Olostep
@nolanpierce03 Great question! The typical mental model is
Parsers: Ideal for high-volume, consistent, recurring data collection (example use cases: price trackers, aeo platforms, brand monitoring etc.)
LLM Extraction: Ideal for flexible extraction needs or websites with changing structures (example use cases: typo analyzer, hallucination detector, ai research)
Sharing some docs here
And some other examples of use cases here
Does it also preserve enough page context for AI agents to understand relationships between different pieces of web data?
Olostep
@jason_scott8 Good question; understanding the page and overall context is important for AI agents. We have a few endpoints for that, like /maps (to map out a website's structure) or /crawls (to recursively explore a domain). Instead, when an AI agent already knows which pages are of interest, the default returned format is markdown -- which compresses the overall page structure but maintains enough context to understand relationships
PixelApps
Among everything I've tried to scrape content of web, Olostep has been the most powerful. You could literally to 10K urls just within a few minutes. I also find endpoints like /answers that gets me the direct answer to questions on web without writing my own search logic, and they work pretty fast.
Congrats on the launch.
Olostep
@samyakk Thanks!
Hey, Congrats with the launch! Interesting project. Would love to know what are Olosteps main differences with other scraping workflows on the market?
the monitor piece is the one id want to hear most about. diffing a page is easy, deciding what counts as a change is the actual problem. my first attempt at this fired on every deploy becuase nav markup and timestamps moved, and i had it muted inside a week, which is the worst possible outcome since the alert is still technically working. whats the unit of identity for a monitored page over time? if a site reorganises its url structure or moves the same content behind a different route, does olostep read that as the same page changed or as a page gone. that one decision is what determines whether monitoring survives six months of contact with a real site. and are extraction schemas versioned? when a field stops appearing i want to know whether the site changed or my schema did, and most tools collapse both into null.