Ex-Twitter CEO Launches Parallel AI with $100M of Series A

Parallel Web Systems, a startup founded by former Twitter CEO Parag Agrawal, has secured US$100m in a Series A funding round.

The investment will help advance the development of web search infrastructure for AI agents and strengthen partnerships with online content owners, Agrawal told Reuters.

The Series A round was jointly led by venture capital firms Kleiner Perkins and Index Venture, with continued support from existing investors including Khosla Ventures.

Announcing the raise on LinkedIn, the company stated: “Our mission is to keep the web open, transparent and competitive. 

“We build the best infrastructure for AI agents, applications and systems to access and think with the web. Our team is lean. Our ambitions are big.”

According to a report by The Economic Times, the latest funding round values the company at about US$740m, following a previous US$30m raise in January 2024.

In an interview with Reuters, Parag explained that the company’s enterprise clients rely on Parallel to power AI agents capable of writing software code, analysing sales data, and assessing risk in insurance underwriting.

He added that access to high-quality web data remains essential across many industries: “How many jobs are there where we could turn off web access and ask you to do the same job fully?

“You can’t deprive an M&A lawyer from not being able to use the web, why would you deprive their agents?”

What is Parallel Web Systems?

Founded in 2023, Parallel Web Systems officially launched in August 2025 and is described as the “only AI system to outperform both humans and leading AI models like GPT-5 in the most rigorous deep web research benchmarks”.​

The company develops application programming interfaces (APIs) that enable AI systems to search the live web and retrieve up-to-date information for completing specific tasks.​

Its flagship product suite includes eight specialised AI research engines for a range of computational applications, with Parallel claiming that its technology exceeds the performance of top AI models in web research evaluations.​

At the beginning of November, the firm unveiled the Parallel Search API, billing it on LinkedIn as “the most accurate web search for AI agents, built using our proprietary web index and retrieval infrastructure”.

Unlike traditional search engines that rank links for users to click, Parallel’s technology delivers optimised content tailored for direct input into an AI model’s context window.​

According to the company, this approach minimises “hallucinations” – false or misleading information – and drives down operational costs for customers.​

Some of the newly raised funds will be used to tackle the growing challenge of web content increasingly locked behind paywalls and login screens as more sites restrict automated AI access.​

In his interview with Reuters, Parag stated that Parallel aims to build an “open market mechanism” – an economic model designed to reward websites financially for keeping their content accessible.​

Multi-functional product development

Pranay Reddy Samala, a Member of the Technical Staff at the firm, said on LinkedIn: “When we first built the Monitor API, we made the classic mistake of thinking we knew what it was for. 

“We opened up access to the team, left for lunch and came back to see that our colleagues had begun testing it out for all kinds of needs.”

He highlighted several tools that his team found valuable, including The Roommate Finder, which succeeded in identifying a match within just a couple of days and The Price Hawk, designed to monitor wish lists for the best available deals.​​

“After watching our team’s creativity run wild, we realised we couldn’t be gatekeeping this. So we launched the API version,” Pranay added.

“Because the best use cases are the ones that we haven’t even thought of yet.”