Parallel
About[About](https://parallel.ai/about)Search[Search](https://parallel.ai/products/search)Pricing[Pricing](https://parallel.ai/pricing)Blog[Blog](https://parallel.ai/blog)Docs[Docs](https://docs.parallel.ai/home)
[Start Building]
[Menu]

# Parallel raises $100M Series A to build web infrastructure for agents

Tags:Fundraise
Reading time: 3 min
Parallel raises $100M Series A to build web infrastructure for agents

Announcing our $100 million Series A at a $740 million valuation to build the web for its second user: AIs. The round was co-led by Kleiner Perkins and Index Ventures, with participation from Spark Capital and support from existing investors Khosla Ventures, First Round Capital, and Terrain. Mamoon Hamid of Kleiner Perkins joins Vinod Khosla, Shardul Shah and Josh Kopelman on the board.

## **The web’s second user: AI**

Two years ago, when we started the company, there weren’t any agents on the web. The prevailing view was that large language models would render the web obsolete. Why would agents need to search when every model had the entirety of the internet in its training data?

We believed the opposite: AIs would use the web far more than humans ever have.

## **Quality is everything**

Today, we offer two types of products, all of which are best-in-class for quality and accuracy of outputs:

  • - **Web Tools** built as primitives for search[search]($https://parallel.ai/blog/introducing-parallel-search), extraction[extraction]($https://docs.parallel.ai/extract/extract-quickstart), and retrieval for agents to use.
  • - **Web Agents **built for structured enrichment[structured enrichment]($https://parallel.ai/blog/parallel-task-api), deep research[deep research]($https://parallel.ai/blog/deep-research-benchmarks), and workflow automation[workflow automation]($https://parallel.ai/blog/case-study-gumloop).

Agents are our users. AI-native builders are our customers.

The most sophisticated builders choose us: Clay, Sourcegraph, Owner, Starbridge, Actively, Genpact, and leading Fortune 100 companies. They've tested alternatives and understand their agents' needs. Whether it's Claygent[Claygent]($https://www.clay.com/claygent) powering GTM workflows, Amp[Amp]($https://ampcode.com/)'s coding agent solving bugs, helping lawyers find precedent, Starbridge[Starbridge]($https://parallel.ai/blog/case-study-starbridge) discovering government RFPs, or a Fortune 100 insurer underwriting claims. Our customers understand something fundamental: if your agent doesn't have good, fresh, accurate data from the web, all else downstream doesn’t matter.

Parallel drops hallucination rates[drops hallucination rates]($https://parallel.ai/blog/benchmarks-task-api-sealqa) across workflows by organizing and bringing forth the right context from the web. Every decision, by an agent or the human, is downstream of the quality of the data it uses. When every company is racing to deploy AI, competitive advantage comes down to who has better access to accurate information.

## **Search for AIs is very different**

Search[Search]($https://parallel.ai/products/search) for agents differs fundamentally from search for humans. Instead of just keyword queries, AIs can ask declarative queries. Instead of ranking URLs that humans might click, we're identifying the optimal tokens from the web to place in an agent's context window. Instead of being limited to a few hundred milliseconds and a limited amount of compute, we can choose to flexibly allocate computation and time in order to improve outputs.

We’ve built the only web APIs that are optimized to be used as tools within AI agents, and they are used at scale including by our own search agents. Building this product is only possible through innovations across crawling, indexing, retrieval and ranking - each purpose-built for AIs as the primary user.

## **The web must remain open to AIs**

As the web's primary user shifts from humans to AIs, business models built on human attention are challenged. By default, the web trends towards zero-sum: paywalls, private data silos, and gated access. We’ve specifically designed our APIs and systems to not just serve AIs as customers, but also provide content and data owners incentives to continue publishing on the open web, and providing all AI agents broad access.

Our mission is to ensure an open, transparent, and competitive web that can be used by all AIs.

## **Join us**

In the past, our team built key infrastructure, systems, and markets that powered the human web.

Now we have a new user to build for.

The AI web is in its infancy. There is so much more to build.

If you, like us, are obsessed with shaping the future of the web for AIs, we’d love to hear from you[we’d love to hear from you]($https://jobs.ashbyhq.com/parallel).

Parallel avatar

By Parallel

November 12, 2025

## Related Posts34

How Macroscope reduced code review false positives with Parallel
Parallel avatar

- [How Macroscope reduced code review false positives with Parallel](https://parallel.ai/blog/case-study-macroscope)

Reading time: 2 min
Product release - Parallel Search API
Parallel avatar

- [Introducing Parallel Search: the highest accuracy web search API engineered for AI](https://parallel.ai/blog/introducing-parallel-search)

Tags:Benchmarks
Reading time: 7 min
Benchmarks: SealQA: Task API
Parallel avatar

- [Parallel processors set new price-performance standard on SealQA benchmark](https://parallel.ai/blog/benchmarks-task-api-sealqa)

Tags:Benchmarks
Reading time: 3 min
Introducing LLMTEXT, an open source toolkit for the llms.txt standard
Parallel avatar

- [Introducing LLMTEXT, an open source toolkit for the llms.txt standard](https://parallel.ai/blog/LLMTEXT-for-llmstxt)

Tags:Product Release
Reading time: 7 min
Starbridge + Parallel
Parallel avatar

- [How Starbridge powers public sector GTM with state-of-the-art web research](https://parallel.ai/blog/case-study-starbridge)

Tags:Case Study
Reading time: 4 min
Building a market research platform with Parallel Deep Research
Parallel avatar

- [Building a market research platform with Parallel Deep Research](https://parallel.ai/blog/cookbook-market-research-platform-with-parallel)

Tags:Cookbook
Reading time: 4 min
How Lindy brings state-of-the-art web research to automation flows
Parallel avatar

- [How Lindy brings state-of-the-art web research to automation flows](https://parallel.ai/blog/case-study-lindy)

Tags:Case Study
Reading time: 3 min
Introducing the Parallel Task MCP Server
Parallel avatar

- [Introducing the Parallel Task MCP Server](https://parallel.ai/blog/parallel-task-mcp-server)

Tags:Product Release
Reading time: 4 min
Introducing the Core2x Processor for improved compute control on the Task API
Parallel avatar

- [Introducing the Core2x Processor for improved compute control on the Task API](https://parallel.ai/blog/core2x-processor)

Tags:Product Release
Reading time: 2 min
How Day AI merges private and public data for business intelligence
Parallel avatar

- [How Day AI merges private and public data for business intelligence](https://parallel.ai/blog/case-study-day-ai)

Tags:Case Study
Reading time: 4 min
Full Basis framework for all Task API Processors
Parallel avatar

- [Full Basis framework for all Task API Processors](https://parallel.ai/blog/full-basis-framework-for-task-api)

Tags:Product Release
Reading time: 2 min
Building a real-time streaming task manager with Parallel
Parallel avatar

- [Building a real-time streaming task manager with Parallel](https://parallel.ai/blog/cookbook-sse-task-manager-with-parallel)

Tags:Cookbook
Reading time: 5 min
How Gumloop built a new AI automation framework with web intelligence as a core node
Parallel avatar

- [How Gumloop built a new AI automation framework with web intelligence as a core node](https://parallel.ai/blog/case-study-gumloop)

Tags:Case Study
Reading time: 3 min
Introducing the TypeScript SDK
Parallel avatar

- [Introducing the TypeScript SDK](https://parallel.ai/blog/typescript-sdk)

Tags:Product Release
Reading time: 1 min
Building a serverless competitive intelligence platform with MCP + Task API
Parallel avatar

- [Building a serverless competitive intelligence platform with MCP + Task API](https://parallel.ai/blog/cookbook-competitor-research-with-reddit-mcp)

Tags:Cookbook
Reading time: 6 min
Introducing Parallel Deep Research reports
Parallel avatar

- [Introducing Parallel Deep Research reports](https://parallel.ai/blog/deep-research-reports)

Tags:Product Release
Reading time: 2 min
BrowseComp / DeepResearch: Task API
Parallel avatar

- [A new pareto-frontier for Deep Research price-performance](https://parallel.ai/blog/deep-research-benchmarks)

Tags:Benchmarks
Reading time: 4 min
Building a Full-Stack Search Agent with Parallel and Cerebras
Parallel avatar

- [Building a Full-Stack Search Agent with Parallel and Cerebras](https://parallel.ai/blog/cookbook-search-agent)

Tags:Cookbook
Reading time: 5 min
Webhooks for the Parallel Task API
Parallel avatar

- [Webhooks for the Parallel Task API](https://parallel.ai/blog/webhooks)

Tags:Product Release
Reading time: 2 min
Introducing Parallel: Web Search Infrastructure for AIs
Parallel avatar

- [Introducing Parallel: Web Search Infrastructure for AIs ](https://parallel.ai/blog/introducing-parallel)

Tags:Benchmarks,Product Release
Reading time: 6 min
Introducing SSE for Task Runs
Parallel avatar

- [Introducing SSE for Task Runs](https://parallel.ai/blog/sse-for-tasks)

Tags:Product Release
Reading time: 2 min
A new line of advanced processors: Ultra2x, Ultra4x, and Ultra8x
Parallel avatar

- [A new line of advanced processors: Ultra2x, Ultra4x, and Ultra8x ](https://parallel.ai/blog/new-advanced-processors)

Tags:Product Release
Reading time: 2 min
Introducing Auto Mode for the Parallel Task API
Parallel avatar

- [Introducing Auto Mode for the Parallel Task API](https://parallel.ai/blog/task-api-auto-mode)

Tags:Product Release
Reading time: 1 min
A linear dithering of a search interface for agents
Parallel avatar

- [A state-of-the-art search API purpose-built for agents](https://parallel.ai/blog/search-api-benchmark)

Tags:Benchmarks
Reading time: 3 min
Parallel Search MCP Server in Devin
Parallel avatar

- [Parallel Search MCP Server in Devin](https://parallel.ai/blog/parallel-search-mcp-in-devin)

Tags:Product Release
Reading time: 2 min
Introducing Tool Calling via MCP Servers
Parallel avatar

- [Introducing Tool Calling via MCP Servers](https://parallel.ai/blog/mcp-tool-calling)

Tags:Product Release
Reading time: 2 min
Introducing the Parallel Search MCP Server
Parallel avatar

- [Introducing the Parallel Search MCP Server ](https://parallel.ai/blog/search-mcp-server)

Tags:Product Release
Reading time: 2 min
Starting today, Source Policy is available for both the Parallel Task API and Search API - giving you granular control over which sources your AI agents access and how results are prioritized.
Parallel avatar

- [Introducing Source Policy](https://parallel.ai/blog/source-policy)

Tags:Product Release
Reading time: 1 min
The Parallel Task Group API
Parallel avatar

- [The Parallel Task Group API](https://parallel.ai/blog/task-group-api)

Tags:Product Release
Reading time: 1 min
State of the Art Deep Research APIs
Parallel avatar

- [State of the Art Deep Research APIs](https://parallel.ai/blog/deep-research)

Tags:Benchmarks
Reading time: 3 min
Introducing the Parallel Search API
Parallel avatar

- [Introducing the Parallel Search API ](https://parallel.ai/blog/parallel-search-api)

Tags:Product Release
Reading time: 2 min
Introducing the Parallel Chat API - a low latency web research API for web based LLM completions. The Parallel Chat API returns completions in text and structured JSON format, and is OpenAI Chat Completions compatible.
Parallel avatar

- [Introducing the Parallel Chat API ](https://parallel.ai/blog/chat-api)

Tags:Product Release
Reading time: 1 min
Parallel Web Systems introduces Basis with calibrated confidences - a new verification framework for AI web research and search API outputs that sets a new industry standard for transparent and reliable deep research.
Parallel avatar

- [Introducing Basis with Calibrated Confidences ](https://parallel.ai/blog/introducing-basis-with-calibrated-confidences)

Tags:Product Release
Reading time: 4 min
The Parallel Task API is a state-of-the-art system for automated web research that delivers the highest accuracy at every price point.
Parallel avatar

- [Introducing the Parallel Task API](https://parallel.ai/blog/parallel-task-api)

Tags:Product Release,Benchmarks
Reading time: 4 min
![Company Logo](https://parallel.ai/parallel-logo-540.png)

Contact

  • hello@parallel.ai[hello@parallel.ai](mailto:hello@parallel.ai)

Products

  • Search API[Search API](https://parallel.ai/products/search)
  • Extract API[Extract API](https://docs.parallel.ai/extract/extract-quickstart)
  • Task API[Task API](https://docs.parallel.ai/task-api/task-quickstart)
  • FindAll API[FindAll API](https://docs.parallel.ai/findall-api/findall-quickstart)
  • Chat API[Chat API](https://docs.parallel.ai/chat-api/chat-quickstart)
  • Monitor API[Monitor API](https://platform.parallel.ai/play/monitor)

Resources

  • About[About](https://parallel.ai/about)
  • Pricing[Pricing](https://parallel.ai/pricing)
  • Docs[Docs](https://docs.parallel.ai)
  • Status[Status](https://status.parallel.ai/)
  • Blog[Blog](https://parallel.ai/blog)
  • Changelog[Changelog](https://docs.parallel.ai/resources/changelog)
  • Careers[Careers](https://jobs.ashbyhq.com/parallel)

Info

  • Terms[Terms](https://parallel.ai/terms-of-service)
  • Privacy[Privacy](https://parallel.ai/privacy-policy)
  • Trust Center[Trust Center](https://trust.parallel.ai/)
![SOC 2 Compliant](https://parallel.ai/soc2.svg)
LinkedIn[LinkedIn](https://www.linkedin.com/company/parallel-web/about/)Twitter[Twitter](https://x.com/p0)

Parallel Web Systems Inc. 2025