Parallel Web Systems, the AI search infrastructure startup founded by former Twitter CEO Parag Agrawal, has raised $100 million in Series A funding, valuing the company at $740 million. The round was co-led by Kleiner Perkins and Index Ventures, with participation from Khosla Ventures and earlier backers.
The funding accelerates Parallel’s mission to build a new search layer for AI agents—a shift Agrawal believes is fundamental to the future of the internet.
A New Internet Paradigm: AI Agents as the Primary Users
Agrawal argues that the internet is undergoing a massive transition. Where human users once dominated web interactions, AI agents are becoming the new primary users, performing tasks on behalf of individuals and businesses.
Parallel aims to provide the foundational infrastructure for this shift by building APIs that deliver real-time, structured, optimised web data directly into LLM context windows.
This is not traditional search. Instead of ranking links for humans to click, Parallel:
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Fetches high-quality, real-time web data,
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Converts it into AI-optimised “tokens,”
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Reduces hallucinations,
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Improves accuracy,
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Cuts AI operational costs.
Why Parallel Exists
As Agrawal puts it:
“You wouldn’t deprive a lawyer or engineer of internet access. Why deprive AI agents?”
Parallel’s enterprise clients already use the APIs to power:
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AI agents writing and debugging software,
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Sales intelligence workflows,
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Financial and risk analysis tools,
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Research automation systems,
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Customer support copilots.
With AI models increasingly dependent on up-to-date information, Parallel’s infrastructure solves a critical gap: LLMs don't have access to the live web in a clean, structured, compliant way.
The Funding: What Parallel Will Build Next
With fresh capital, Parallel plans to:
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Expand its enterprise agent infrastructure
Build larger-scale data pipelines capable of supporting millions of AI transactions per second. -
Double down on product development
Add more data types, domain-specific datasets, and fine-grained retrieval tools. -
Develop a new economic model for publishers
As websites lock content behind paywalls to block AI scrapers, Parallel wants to create an “open market mechanism” that fairly compensates publishers for structured data access. -
Grow its engineering and research teams
With a focus on search, retrieval, token optimisation, and copyright-safe data sourcing.
A Founder With Rare Technical Depth
Agrawal’s background uniquely positions him to build this infrastructure. As Twitter’s former CTO and later CEO, he oversaw some of the world’s most complex data pipelines.
Before that, he was a distinguished engineer who worked on scalable search systems, distributed databases, and ML optimisations. Parallel merges that expertise with a future-facing vision of AI as the interface to the web.
A Rising Giant in AI Infrastructure
Parallel’s timing is impeccable. The AI ecosystem is shifting toward:
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Autonomous agents
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Tool-using LLMs
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Real-time reasoning models
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On-demand search integrations
Every one of these relies on high-quality, structured internet data.
With $100 million now in the bank, Parallel is positioning itself to become the “web engine” for the coming AI age.
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