Inside Ex Twitter CEO’s $100M Funding for New AI Startup

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

The funding will contribute towards building web search infrastructure for AI agents and supporting deals with online content owners, Parag told Reuters.

The Series A round was co-led by investment firms Kleiner Perkins and Index Venture, alongside participation from other existing backers including Khosla Ventures.

Announcing the funding on LinkedIn, the company posted: “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 funding values the company at around US$740m, following an earlier funding of US$30m in January 2024.

In the Reuters interview, Parag said the company's enterprise customers use Parallel to power AI agents that write software code, analyse customer data for sales teams and assess risk for insurance underwriting.

He said that areas where high-quality web data is used in internal systems is critical: “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 was officially launched in August 2025, described as the “only AI system to outperform both humans and leading AI models like GPT-5 on the most rigorous benchmarks for deep web research”.

The company builds application programming interfaces (APIs) that let AI systems search the live web for up-to-date information to complete tasks.

Its flagship offering includes eight specialised AI research engines designed for various computational tasks, with the company claiming that its technology surpasses top AI models in web research benchmarks.

The firm announced the Parallel Search API at the start of November, which it described 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 humans to click, Parallel’s system returns optimised content designed to feed directly into an AI model’s context window.

The company says this approach reduces “hallucinations”, meaning false or misled information, and cuts operational costs for customers.

Some of the funding will help with tackling the challenge of web content being stuck beyond paywalls and login processes, which are being increasingly deployed to prevent AI from scanning the web freely.

Speaking with Reuters, Parag said the company plans to develop an “open market mechanism”, an economic model which gives websites an incentive to keep 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 listed multiple functions his team members found including The Roommate Finder, which successfully identified one in a couple days and The Price Hawk, which monitors wish lists for 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.”