Leeroo is a YC-backed startup building continuously learning AI agents that operate more like human teammates—learning from documentation, feedback, and their own history to improve over time. Here’s co-founder Alireza Mohammadshahi on his journey, lessons learned, and what keeps him building. 👇
Co-founders: 3 (Arshad Shaikh, Alireza Mohammadshahi, Majid Yazdani)
Team Size: 4
Amount Raised: $850K USD (Y-Combinator, Cornerstone VC)
Core Technology: Continuously learning AI agents that learn much like human colleagues— from knowledge bases, human feedback, and even their own past experiences
Table of Contents
From NLP Research to Startup Founding
I originally left my home country at around the age of 22 and moved to Switzerland to pursue a PhD at EPFL, where I focused primarily on natural language processing and AI. I also spent time at several other institutions in Switzerland, including the University of Zurich and IDIAP Research Institute. After four or five years in academia, I transitioned into industry, continuing to work in generative models. One of the more notable places I worked was Meta AI.
While at Meta, I met my co-founder Majid—we collaborated on a project that, at the time, wasn’t called an “LLM-as-a-judge,” but the concept was similar. We were using AI models to evaluate the output of generative models. That particular model has since gained a lot of traction—it’s been downloaded over a million times on Hugging Face.
I think at the moment, there isn’t a significant gap between science and production—at least in the LLM and AI domains. As soon as a paper comes out, it’s often just a matter of days before someone implements it in production. It wasn’t always like this. In the past, it followed the more traditional model—where you'd conduct research, and it might take years before it translated into production. That shift really influenced how I thought about my career.
By the end of my PhD, I was certain: rather than going back to Big Tech, I wanted to start my own company. And it just felt like a golden moment—there are so many tools now that enable you to build products end to end, and I already had the technical depth in LLMs and that broader domain. So I thought, this is the right time to take a bet on a startup. Eventually, Majid, Arshad and I decided to start Leeroo, and we brought on a third co-founder from India, who focuses more on low-level systems and engineering. That’s how we came together.
The Problem: Training AI Agents into Experts
For us, the core problem we're addressing is the disconnect between human domain expertise and what current LLMs or AI agents are capable of.
There are experts in every field—medicine, finance, software engineering—people with deep, valuable knowledge. And then there are large language models, which are generalists. These models are improving rapidly, and sometimes they can even produce expert-level output. But there's no effective way to transfer a domain expert’s knowledge into the model. It’s not predictable or systematic.
The Solution: Continuous Learning AI Agents
What we’re trying to do is bridge the gap between human expertise and agentic AI capabilities.
We want to empower experts to directly and continuously transfer their knowledge into agents—so the agent becomes more like an expert colleague. That’s why we call our agents continuous learning agents. You can interact with them daily, update them with new insights or corrections, and over time they improve—without needing to collect massive datasets, retrain, or fine-tune using heavy compute. Instead, we’ve built a module on the backend—what we call the learning engine—that observes interactions and updates the agent’s knowledge. It reviews everything that happened today and decides, “These are the things I should learn for tomorrow.” That’s our vision: creating AI agents that grow and adapt just like a teammate would.
We're starting with the domain of data and AI—so the first agents we’re building and deploying are for roles like data engineers, data analysts, and machine learning engineers. These agents are designed to operate at an expert level within those specific domains. Once we validate that, the plan is to expand into other areas.
For example, we’re currently working with a company focused on private banking. They shared with us a training package that they normally give to interns to help them prepare for their presentations during their time there. We took that same material and gave it to one of our agents—essentially training it to act like an intern in that specific context. It’s still experimental, but it shows the broader vision: allowing agents to quickly absorb domain-specific knowledge and become high-performing contributors within any field.
