Parallel Web Systems, the AI search startup led by former Twitter CEO Parag Agrawal, has raised $100 million in Series A funding in a round co-led by Kleiner Perkins and Index Ventures.
The raise brings the company’s valuation to around $740 million, according to Reuters reports.
Parallel’s mission is centered on a simple question: if AI agents are becoming core tools for work, why do they still rely on web structures designed only for humans?
Agrawal argues that traditional search engines prioritise pages, ranking, scroll depth and ads, while AI systems need structured, machine-readable information delivered through clean interfaces. Parallel is trying to rebuild this layer from the ground up.
The company’s platform provides APIs that allow AI agents to pull information from live websites, process it into structured formats and feed it directly into tasks such as code generation, legal research, hiring workflows and complex data retrieval.
Rather than scraping static pages or relying on cached data, Parallel aims to give AI systems access to an evolving version of the web.
Why Investors Are Paying Attention
The new funding allows Parallel to expand engineering teams and deepen the systems required to manage real-time web access at scale. It also supports development of what Agrawal calls an open economic model that lets publishers and content owners share material with AI agents under transparent terms.
Investors see this as an emerging category in the AI stack. Existing large models rely heavily on training data that quickly becomes outdated, while enterprise applications increasingly need up-to-the-minute information.
The pressure for quality, recency and legal certainty has created demand for infrastructure that sits between websites and AI agents, something Parallel is positioning itself to supply.
Reuters noted that Parallel already serves customers in areas such as enterprise data workflows and code-related research tasks. While the company has not disclosed names, early users reportedly include firms that require continuous updates from news, financial data and technical documentation.
A Space Filled With Open Questions
Parallel’s approach raises questions across regulation, copyright and economic incentives. Giving AI systems structured access to the live web involves negotiating rights with publishers who are still determining how to handle AI traffic.
Parallel aims to position itself as a conduit rather than an extractor, but the broader ecosystem will shape how feasible this is.
Another challenge is technical. Delivering real-time access with reliability, speed and accuracy demands infrastructure normally associated with search engines. The company’s ability to match those expectations while maintaining cost efficiency will be a crucial factor.
What Comes Next
Parallel plans to grow its customer base, expand its content partnerships and open additional APIs that let developers build autonomous agents that depend on fresh web data.
The bigger question is whether this layer becomes a foundational part of the AI stack: a service that every agent relies on, similar to how modern apps rely on cloud providers.
Agrawal’s return to the tech landscape through Parallel reveals a shift in focus for post-big-tech founders. Instead of consumer apps or social platforms, the new frontier lies in infrastructure that supports the next generation of AI systems.
Parallel now has the capital to pursue that vision, but the path will depend on partnerships, regulation and the company’s ability to deliver trust at scale.
