Olostep

@olostep search for recent case studies on AI adoption in manufacturing, then scrape the top 3 results and summarize key ROI metrics mentioned.

Searched 'AI adoption manufacturing case studies 2024' and scraped three articles. Common ROI themes: 18–35% reduction in defect rates (two automotive suppliers), 22% faster changeover times (packaging line automation), and 12–40% labor reallocation to higher-value tasks. Two studies cited payback periods under 18 months; one noted integration costs exceeded initial estimates by 30%. Retrieved IDs stored for full-text access. Operation consumed 35 credits (5 search + 10 per scraped page).

This example chains Search Web and Scrape Page to show Olostep's research workflow. Each search costs 5 credits; scraping adds 10 per page. The MCP returns retrieve IDs so you can pull full HTML or markdown later without re-scraping. Watch your credit balance if running many multi-page workflows in a single session.

@olostep map the documentation site docs.example.com and return all URLs under /api/reference, ranked by depth.

Mapped docs.example.com and found 147 URLs. Filtered to /api/reference: 34 pages ranked by depth. Top-level index at depth 1, endpoint pages at depth 2–3, code-sample subpages at depth 4. Continuation cursor provided for next 50 URLs if needed. Operation consumed 20 credits. Retrieve IDs available for any page you want to scrape in full.

Map Website discovers site structure without scraping every page, making it cheaper than a full crawl when you only need a URL inventory. Ranking by depth helps prioritize high-level docs. If you need page content afterward, call Scrape Page or Retrieve Content with the returned IDs—mapping alone doesn't pull bodies.

@olostep answer this question: What are the top three open-source vector databases by GitHub stars as of this month, and what embedding dimensions does each support?

Researched GitHub and project docs. As of December 2024: (1) Milvus – 31.2k stars, supports 128–2048 dimensions; (2) Weaviate – 12.8k stars, supports up to 65536 dimensions; (3) Qdrant – 11.4k stars, supports 64–4096 dimensions. Sources: github.com/milvus-io/milvus, weaviate.io/developers/weaviate/config-refs/schema, qdrant.tech/documentation/concepts/collections. Operation took 8 seconds and consumed 25 credits.

Answer Question is Olostep's synchronous research tool—it searches, browses, and synthesizes in one call, returning sources. Typical runtime is 3–30 seconds; credit cost scales with complexity. Use this when you need a bounded factual answer fast. For ongoing monitoring or large-scale scraping, consider the asynchronous crawl tools instead.

When Olostep beats manual research for market scans

A 6-person SaaS team needs competitor pricing, feature lists, and recent blog posts scraped before their Q2 roadmap session. Olostep's Answer Question tool runs the bounded research task in under 30 seconds and returns structured sources—faster than assigning a PM to click through ten sites. The credit meter matters here: if you're running this scan weekly, budget 200-500 credits per quarter depending on page depth. The Map Website tool finds every pricing tier URL without guessing nav paths. This works when the intel need is episodic and the questions are concrete. If your team runs competitive research daily or needs live monitoring, you'll burn credits fast and should consider a dedicated intel platform. For quarterly or monthly planning cycles where speed and structure beat cost, Olostep closes the loop in one standup.

Why Olostep fits one-off product questions, not ticket volume

A 3-person support team gets a ticket asking whether a partner API supports OAuth2—documentation is scattered across three vendor sites. Olostep's Search Web and Scrape Page tools pull the answer in 10 seconds, cite the source URLs, and the agent pastes it into the ticket. This scenario works because the question is bounded, the answer lives on public pages, and the team handles 20-40 tickets a day, not 200. The credit cost per lookup is predictable, and the Retrieve Content tool lets you cache answers for repeat questions without rebilling. The threshold: if more than 30% of your tickets need web research, or if answers require login-walled docs, Olostep becomes expensive or impossible. For small teams triaging occasional product-integration questions where the public web holds the answer, it's faster than tab-switching through five browser windows.

When Olostep accelerates deal research without analyst overhead

A 4-person sales team qualifies enterprise leads by researching the prospect's tech stack, recent funding, and compliance posture before the first call. Olostep's Answer Question tool runs the discovery in 20 seconds and returns a summary with sources—no need to hire a sales engineer or wait on a BDR to compile a deck. The Map Website tool crawls the prospect's careers page to infer team size and growth. This works when your deal volume is 10-30 qualified leads per month and each research task is a one-time lookup. If you're running discovery on 100+ leads weekly, the credit burn outpaces a junior analyst's salary. The buying call: if your ACV justifies 15 minutes of prep per lead and you don't have a dedicated research function, Olostep turns web research into a tool call instead of a calendar block.