Parallel has secured a $100 million Series A round at a $740 million valuation, marking one of the largest early-stage financings in the emerging market for AI-native web infrastructure. The round was co-led by Kleiner Perkins and Index Ventures, with participation from Spark Capital and continued support from existing backers Khosla Ventures, First Round Capital, and Terrain. As part of the investment, Kleiner Perkins partner Mamoon Hamid will join the company’s board alongside Vinod Khosla, Shardul Shah, and Josh Kopelman.
Founded two years ago, Parallel has positioned itself around a core prediction: that the web’s next primary users will not be humans, but AI agents. At a time when many believed that large language models would diminish the importance of the web, the company bet that agents would rely on it even more, requiring new infrastructure built for machine-level search, retrieval, and context assembly.
Parallel’s platform now spans two major product categories: Web Tools, which act as primitives for search, extraction, and retrieval, and Web Agents, which support deep research, enrichment, and automated workflows. These systems are designed with a focus on accuracy and context quality—areas the company argues are essential as enterprises look to deploy reliable AI agents in production environments.
The company’s technology is already used by fast-growing AI-native builders, including Clay, Sourcegraph, Owner, Starbridge, Actively, and Amp, as well as major Fortune 100 enterprises. Customers use Parallel’s infrastructure for applications ranging from GTM automation and coding assistance to legal research, government RFP discovery, and enterprise underwriting. According to the company, the ability to consistently deliver fresh, high-accuracy data from the web directly influences downstream decision-making for both agents and humans.
Parallel distinguishes its approach by building a search designed explicitly for AI. Rather than returning ranked links for humans to click, its system identifies optimal tokens to fit into an agent’s context window. It operates without the strict latency constraints of human-oriented search. The company claims its APIs are the only ones designed specifically for use within AI agents, made possible by proprietary advancements in crawling, indexing, retrieval, and ranking—all architected with AIs as their primary users.
Beyond infrastructure, Parallel positions itself as an advocate for keeping the web open to AI. As user behavior shifts from human browsing to agent-driven access, the company warns that traditional attention-based business models could lead sites toward paywalls, gated systems, or fragmented data silos. Its platform is designed to support an open ecosystem by creating incentives for publishers and ensuring broad access for AI agents.
Parallel’s team includes veterans who previously built foundational systems for the human-driven web. With this new capital, the company plans to accelerate development of infrastructure for what it calls the “AI web,” a rapidly forming layer of machine-to-machine interaction that it believes will define the next era of internet use. The company is actively hiring across engineering, product, and AI research roles as it scales to meet demand from enterprises building agent-powered applications.
