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Parallel Web Systems, the AI startup from former Twitter CEO Parag Agrawal, has secured $100 million in a Series A round co-led by Kleiner Perkins and Index Ventures. The company is building a new layer of web infrastructure designed for AI agents to search and interact with live, accurate data.
Parallel Web Systems, has secured $100 million in a Series A funding round to build web infrastructure designed for artificial intelligence agents.
Founded by former Twitter CEO Parag Agrawal, the two-year-old startup is developing application programming interfaces (APIs) and a purpose-built index to provide AI systems with real-time access to reliable online information. The funding round was co-led by Kleiner Perkins and Index Ventures, with participation from Spark Capital and existing investors including Khosla Ventures, valuing the company at $740 million.
The Update: Parallel has raised $100 million in a Series A round to accelerate the development of its web infrastructure for AI agents. The platform addresses the limitation of large language models that rely on static, outdated datasets, which can lead to inaccurate or "hallucinated" outputs. By creating a new layer of web plumbing, Parallel enables AI agents to access and process fresh, reliable online information for tasks such as software development, sales data analysis, and insurance risk assessment.
The funding will be used to expand agent capabilities, develop faster web primitives, and scale its web index. Kleiner Perkins partner Mamoon Hamid will join the company's board. In a statement, Hamid compared the investment to his firm's early backing of Google, noting, "26 years later, Parallel is indexing the web for its 'next' user: AI agents." The company's first products, including a web search API for agents, were launched in August 2025.
Why it Matters: Parallel is tackling a fundamental challenge in the operationalisation of AI: the need for continuous access to high-quality, live data. As enterprises deploy AI agents for increasingly complex and mission-critical workflows, their effectiveness is constrained by the quality of the data they can access. Parallel's approach of building a purpose-built infrastructure for AI agents, rather than adapting human-centric search, could provide a significant competitive advantage to businesses that rely on it. The startup also aims to create an economic model that incentivises content owners to keep their data accessible to AIs, addressing the growing tension between AI developers and publishers.
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