Accel doubles down on Fibr AI as agents turn static websites into one-to-one experiences

While advertising and targeting have become increasingly personalized, the website – the final destination for that traffic – has remained largely static. Fibr AI wants to bridge that gap by using AI agents to turn generic web pages into one-to-one experiences tailored to each visitor, a proposition that has prompted Accel to double down on business.

Accel led Fibr AI’s $5.7 million seed round, following an earlier pre-seed investment of $1.8 million in 2024. The new funding also included participation from WillowTree Ventures and MVP Ventures, in addition to Fortune 100 operators joining as angel investors and advisors, bringing the startup’s total funding to $7.5 million.

For large companies, the gap between increasingly personalized ads and largely generic website experiences has traditionally been filled by a mix of personalization software, engineering teams and marketing agencies – a model that is slow, expensive and difficult to scale. While ads can be instantly tailored to different audiences, changing what happens once a visitor lands on a site often requires weeks of coordination and limits teams to running just a handful of experiments per year. Fibr AI argues that this people-heavy business model no longer works. Instead, the startup uses autonomous AI agents to infer intent, generate variations, and continuously optimize pages in real time.

Fibr AI replaces the desk- and engineering-heavy model with autonomous systems that work continuously, co-founder and CEO Ankur Goyal (pictured above, right) said in an interview.

“We are [the] software, and the agency is the workforce of agents that we deploy,” Goyal told TechCrunch, adding that this allows Fibr AI to run thousands of experiments in parallel instead of a few dozen per year.

Adoption was slow at first. Founded in early 2023 by Goyal and Pritam Roy (pictured above, left), Fibr AI had just one or two customers for much of its first two years as companies took time to evaluate the approach. That started to change last year, Goyal said, as adoption increased among major U.S. companies, including banks and health care providers, bringing the total number of customers to 12.

“We are an infrastructure afterthought layer,” Goyal told TechCrunch. “Once it’s set up, no one wants to think about it anymore.” That dynamic, he added, has led Fibr AI to sign three- to five-year contracts with large enterprises, which tend to view website infrastructure as something to be standardized rather than constantly rethought.

On a technical level, Fibr AI functions as a layer on top of an existing website, connecting to a company’s advertising, analytics and customer data systems to understand how visitors arrive and what they are likely to be looking for. The AI ​​agents then collect and modify page content such as text, images, and layout, treating each URL as a system that continuously learns and optimizes rather than as a fixed page. Instead of relying on manually configured rules or sequential A/B testing, the platform runs large numbers of micro-experiments in parallel and systematically updates experiences as traffic flows in through different channels.

This shift has direct cost implications for large enterprises. Traditional website personalization typically combines software licensing with agency recruitment costs and engineering time, tying costs to people rather than results. Goyal said companies are increasingly evaluating the Fibr AI platform based on cost per experiment and impact of conversion, rather than the number of tools or people involved.

For Accel, that business model – and not the AI ​​buzz – was crucial in the decision to reinvest. “Advertising today is one-to-one, but when users land on a website, it becomes one-to-many,” said Prayank Swaroop, partner at Accel. “You can create hundreds of ads for different audiences, but they all still end up on the same page.” Fibr’s ability to turn that dynamic into one-to-one personalization, he said, stood out because it removed the agency and technology bottlenecks that typically limit how far companies can experiment.

Swaroop added that early enterprise adoption, especially among banks and healthcare companies, helped validate the thesis. “These are regulated, conservative industries,” he said. “When they start saying, ‘We need this and we’re willing to pay for it,’ then we feel confident.”

Future-proof for the age of agent trading

While the bulk of Fibr AI’s business today is driven by personalizing experiences for human visitors, Accel and Fibr AI also see potential in the way AI agents are beginning to mediate online discovery. As users research, compare and shortlist more and more products using large language models and AI chatbots, including OpenAI’s ChatGPT, before visiting a website, the ability of sites to adapt based on what a visitor (or an AI system acting on their behalf) already knows may become increasingly important over time.

“That part is still early,” Swaroop said, “but the companies that are building for today’s needs while being ready for tomorrow’s change are the ones we want to support.”

With the new funding, Fibr AI plans to focus on growing its sales and customer-facing teams in the US, while continuing to build out its technical base in India. The San Francisco-headquartered startup has an office in Bengaluru, with 17 of its approximately 23 employees in India and the remaining six in the US.

Goyal said the startup is targeting about $5 million in annual recurring revenue and reaching about 50 enterprise customers by the end of this year.

Fibr AI is entering a space long dominated by incumbents like Adobe and Optimizely, which offer experimentation and personalization tools to large enterprises. But both Goyal and Swaroop argued that these platforms are limited by the way they are built and sold, typically relying on marketing agencies and engineering teams to configure and operate them. That model, they said, makes it difficult to move quickly or scale experiments, even as customer acquisition and messaging have become increasingly dynamic.

“Established companies have been slow to bring products to market,” Swaroop said, adding that even when new features appear, they often come years after demand has changed.