About KelAI
KelAI builds an AI-native research engine that helps hedge funds, traders, and institutional investors generate, test, and validate trading ideas. It was founded by former WorldQuant portfolio manager Jeremie Cohen and is in YC's Spring 2026 batch. In July 2026 it raised a $5M seed round with Frst, Y Combinator, and Robinhood Ventures.
What is KelAI?
What is KelAI?
KelAI describes itself as the AI quant, an autonomous alpha engine for funds and traders. Investment teams now have more data, tools, and compute than ever, but research still runs across siloed systems and slow workflows. KelAI aims to turn that fragmented process into a single AI-native engine that continuously finds, tests, and validates trading signals.
According to The SaaS News, the platform integrates into a fund's existing data and research workflows so AI agents can work autonomously inside them. The focus is on the full research loop, from idea generation to backtesting and validation, rather than a single analytics feature.
KelAI was founded by Jeremie Cohen, previously a portfolio manager and head of event-driven systematic strategies at WorldQuant. The company is based in New York and is part of Y Combinator's Spring 2026 batch. In July 2026 it announced a $5M seed round with participation from Frst, Y Combinator, Robinhood Ventures, and angel investors from AI and finance. KelAI said it planned to spend the following six months improving the product and selectively expanding its New York team.
Pros
- Built by a former systematic portfolio manager with hedge fund domain experience
- Covers the research loop from idea generation to validation
- Integrates with a fund's existing data and research stack
- Agents can run research workflows autonomously
Cons
- Aimed at institutional investors, not retail users
- No public pricing on the website
- Early-stage product with a small team as of the seed announcement
How to Use KelAI
- 1Contact the team
Reach out through the KelAI website to discuss your fund's research process.
- 2Connect data sources
Integrate KelAI with your existing datasets and research tools.
- 3Set research objectives
Define the markets, strategies, or signal types you want the engine to explore.
- 4Review validated ideas
Evaluate the signals KelAI generates and tests before taking them into production.