The round signals momentum for AI-native search tools and AI agent capabilities in web indexing and retrieval.

Mamoon Hamid of Kleiner Perkins joined the company’s board as part of the post-money round.

Parallel Web Systems, an AI search startup founded by former Twitter CEO Parag Agrawal, has raised a $100 million Series A at a $740 million post-money valuation, signaling momentum for AI-native search tools.

The company aims to address web content paywalls and login barriers by developing an open market mechanism to incentivize publishers to keep content accessible to AI systems, though specifics were not disclosed.

This effort fits a broader trend where AI agents become primary web users, creating demand for reliable, up-to-date data feeds for AI-enabled workflows.

The new capital will be used for product development and customer acquisition, with exploration of approaches to support publishers in keeping content AI-accessible.

Parallel’s mission is to enable AI agents to conduct deep web research by trawling the open web to assemble detailed reports.

The funding was led by Kleiner Perkins and Index Ventures, with participation from Spark Capital and existing backers including Khosla Ventures.

Additional backers include Spark Capital, with ongoing support from prior investors such as Khosla Ventures, First Round, and Terrain.

The company was founded two years ago, launched products in August 2025, and previously raised $30 million in January 2024.

Customers use Parallel-powered AI agents for code writing, analyzing sales data, and assessing underwriting risk, underscoring the need for high-quality web data.

The announcement took place in San Francisco at Newcomer’s Cerebral Valley AI Summit, a venue where industry participants discussed the AI investment landscape.

Agrawal argues Parallel’s approach is better than traditional AI-model-integrated web search, focusing on delivering optimized content directly to models rather than ranking links for humans.

The investment is framed as part of a larger ecosystem of AI, search, and tooling for autonomous agents.

While sponsor and podcast sections exist, the core narrative centers on the funding and Parallel Web Systems’ strategic focus.

Parallel builds APIs that let AI systems search the live web and return optimized content tokens to feed into models, aiming to improve accuracy and reduce hallucinations while lowering enterprise costs.

Overall, Parallel aims to architect a web optimized for AI agents to search and retrieve information accurately, addressing both theoretical and practical challenges of agent-based research.