The Parallel Search MCP Server is now live in Devin's MCP Marketplace, bringing high quality web research capabilities directly to the AI software engineer. With a web-aware Devin, you can can ask Devin to: - Search online forums to debug code - Learn from online codebases - Research APIs Learn more: https://lnkd.in/gvcmwrC8
Parallel Web Systems
Technology, Information and Internet
Palo Alto, California 3,297 followers
A parallel web, for AIs
About us
At Parallel Web Systems, we are bringing a new web to life: it’s built with, by, and for AIs. Our work spans innovations across crawling, indexing, ranking, retrieval, and reasoning systems. Our first product is a set of API for AIs to do more with web data.
- Website
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https://www.parallel.ai/
External link for Parallel Web Systems
- Industry
- Technology, Information and Internet
- Company size
- 11-50 employees
- Headquarters
- Palo Alto, California
- Type
- Privately Held
Locations
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Primary
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Palo Alto, California 94305, US
Employees at Parallel Web Systems
Updates
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Today, we're sharing state-of-the-art benchmarks for the Parallel Search API. We evaluated the Parallel Search API on the WISER-Search benchmark, by comparing three different web search solutions (Parallel MCP server, Exa MCP server/tool calling, LLM native web search) across four different LLMs (GPT 4.1, O4-mini, O3, Claude Sonnet 4). Our Search MCP Server demonstrates superior performance while being up to 50% cheaper than LLM native web search implementations. Learn More: https://lnkd.in/g6gd6k-q
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The Parallel Task API now supports Tool Calling via MCP Servers. With a single API call, you can choose to expose tools hosted on external MCP-compatible servers and invoke them through the Task API. This allows Parallel agents to reach out to private databases, code execution sandboxes, or proprietary APIs - without custom orchestrators or standalone MCP clients. Learn more: https://lnkd.in/g9aDxW8a
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The Parallel Search MCP Server is now live - allowing developers to easily integrate the Parallel Search API with any MCP-aware LLM. With a simple config change, LLMs get access to instant dense, citation-rich passages ranked for LLM reasoning. Learn More: https://lnkd.in/g6eMeFPQ
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Parallel Web Systems reposted this
As we scale both our ambitions and our team at Parallel Web Systems, we're hiring builders who want to bring a new web to life. We’re looking for people eager to push the frontier in their domains: Design Engineer – build new interfaces for the web Research Scientist – scale inference and retrieval over web data Go-to-Market & DevRel – create new markets so companies, developers, and AIs can access our technology We’re a small, in-person team in Palo Alto. Couldn’t be a more interesting time to join.
At Parallel Web Systems, we are bringing a new web to life: it’s built with, by, and for AIs. After more than a year of heads-down building, our first web-research APIs are live on www.parallel.ai. As we scale with customers, we’re growing outside core engineering for the first time and hiring our first: Design Engineer, DevRel Engineer, Research Scientist, GTM Lead. Details → https://lnkd.in/gXRmEqvS
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Source Policy is now available with the Parallel Task API and Search API. Source Policy lets you define exactly which domains your research should include and exclude. This gives you granular control over what web sources AI agents access and how results are prioritized. Learn More: https://lnkd.in/gCGYcNe3
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Introducing the Parallel Task Group API. Easily create, monitor, and collect results from large parallel web research workloads. Learn more: https://lnkd.in/gaGBDtY7
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Parallel is excited to partner with Lindy to bring state-of-the-art web research to AI-native workflows and automations!
Self-monitoring AI just dropped Latest Lindy updates: 🎯 Agent Monitoring - Your agents can now watch other agents work: - Get notified when any agent finishes a task - See the complete work history of any agent - Build agents that can turn other agents on and off 📧 New Lead Generation Apps - Hunter, Contactout, and Prospeo integrations for email finding and contact enrichment 🔬 Parallel.ai Integration - Structured data enrichment for lead research (think Perplexity for business data) 📅 Outlook Calendar Trigger - Agents can now trigger from Outlook events Pro tip: Combine agent monitoring with lead enrichment apps to build self-optimizing sales workflows. Meta mode: activated. Try it today: lindy.ai
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Lindy AI agents can now access Parallel powered Actions. Use Parallel in Lindy for: 🔍 Web Enrichment - Transform inputs into rich structured data from the web. 💬 Chat with Web - Get instant answers powered by web research. Perfect for automatically enriching sales leads, monitoring competitors, and researching prospects on autopilot. Learn More: https://lnkd.in/g7fu6Wf5
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At Parallel, we are building state-of-the-art systems to query the web. These systems require not only price performance, but also true verifiability. With Basis, enterprises can run efficient human-in-the-loop workflows with citations, reasoning, excerpts, and calibrated confidences per output. Learn More: https://lnkd.in/ghBZd2zq
Trustworthy AI needs proof of work. As AI agents start doing real work on our behalf — answering questions, making decisions — accurately attributing and evaluating their knowledge becomes foundational to building trust, transparency, and verifiability. Excited to share what we've been building at Parallel Web Systems with Parag Agrawal - an evidence-driven response grounding system that sets new standards for agentic proof of work. • Robust data attribution — every response is grounded in explicitly cited sources, with verbatim excerpts and context-rich snippets. • Calibrated confidences — answers with confidence levels that reflect how well they’re grounded in accuracy. This isn’t just about generating answers. It’s about building systems that know what they know, admit what they don’t, and make it easy for humans to trust and validate the output. Read more on the blog: https://lnkd.in/ea__EypW