Artificial intelligence startup KelAI has secured $5m in seed funding to accelerate development of an autonomous research platform designed for hedge funds and other institutional investors, according to a report by CityBiz.

The funding round was backed by Paris-based venture capital firm Frst, Y Combinator, Robinhood Ventures and a group of angel investors with backgrounds in AI and financial markets. Founded by Jeremie Cohen, KelAI was part of Y Combinator's Spring 2026 accelerator programme.

The New York-based company is aiming to modernise the investment research process by replacing fragmented workflows with an AI-native platform capable of generating, testing and monitoring investment ideas autonomously.

KelAI's technology is designed to integrate multiple stages of the research process—including idea generation, backtesting, signal validation and ongoing monitoring—within a single system. The platform can also connect to an investment firm's proprietary datasets, portfolio constraints, risk parameters and historical research, allowing AI agents to carry out research tasks with limited human intervention.

Announcing the funding, Cohen said artificial intelligence has the potential to fundamentally reshape investment management, arguing that AI can deliver not only productivity improvements but also generate investment ideas at a speed and scale beyond traditional research teams.

He added that while capital markets increasingly have access to vast quantities of data and computing power, research remains fragmented across multiple systems and manual processes, creating an opportunity for purpose-built AI infrastructure.

According to the company, the fresh capital will be used over the coming months to enhance the platform's capabilities, expand its engineering team selectively and support product development.

The fundraising reflects growing investor interest in startups applying generative AI and autonomous agents to institutional investing. A growing number of technology firms are seeking to automate labour-intensive research functions in the belief that AI can shorten the time between identifying an investment hypothesis and deploying it as a live trading signal.

For hedge funds, the potential attraction lies in increasing research capacity, accelerating idea generation and testing a larger number of investment strategies without proportionally expanding research teams.

However, widespread adoption is likely to depend on whether autonomous AI systems can satisfy institutional investors' requirements for transparency, auditability and robust risk management. Investment firms typically require research processes to be fully traceable and repeatable, particularly where AI-generated signals influence portfolio decisions.