MD

We are not short of information. We are buried in it.
There has never been more information available about a company, yet it has never felt harder to understand what actually matters. A dozen apps compete for our attention, and most days, they win. The information is public, but putting it together into a clear picture still takes time that most people do not have.
Between work and the rest of life, few of us can spend hours researching every investment decision. That barrier is real. Even among households at the eightieth percentile of wealth, nearly one in five owns no public equity at all. Investing may be more accessible than it once was, but informed investing still tends to favor people with the time, tools, and people to do the work for them.
Left to do it alone, we all reach for the same shortcuts. We pay attention to the investments already making headlines or moving dramatically because attention is the easiest filter available. We tend to invest close to home, even though the benefits of looking beyond familiar markets have been understood for decades. Losses loom larger than gains, so we hold the sinking positions and hope they recover, while selling the winners early just to lock in the feeling of a gain. We assume strong recent performance will continue, often buying when expectations are highest and future returns are lowest.
That is not ignorance. It is what happens when we are asked to make complicated decisions with limited time, incomplete information, and nothing separating our emotions from our money.
Jim is built to level the playing field.
Jim reads the information you would need to make a decision as soon as it becomes available. The hours you do not have to research, Jim does. The edge that was once reserved for a few on Wall Street now belongs to everyone.
And when you are ready to act, Jim helps keep the decision grounded in facts rather than hype, so the loudest stock is not automatically the first one considered. Jim keeps track of the original reason behind an investment and identifies when that reason has actually changed, rather than reacting to every movement in price. This helps investors avoid holding a losing position out of hope, or selling a strong one early simply to feel safe.
We are a small team, and we built Jim the way we wished investing had been built for us: rigorous, honest, and on the side of ordinary people trying to dig themselves out from under the pile.
That is why we keep building.



Co-founder, Product & Strategy
An AI engineer who ships whole products: the model, the interface, and the story around it. A senior at UC Berkeley in EECS and Economics, he researches natural language processing at Berkeley AI Research on when a model should hedge, and ships just as readily: a document-AI pipeline he built for title insurance cut an eight-hour job to forty-five minutes. He is fluent in markets too, from quant research and a top-1% finish in an algorithmic-trading competition to co-creating and head-TAing Berkeley’s Business and Finance for Engineers. At Jim, he leads product, strategy, design, brand, and marketing, and owns the artificial intelligence: what Jim knows, and how honestly it says it.
Co-founder, Engineering
A serial builder and senior software engineer who takes products from idea to production and puts them in front of real users. He has engineered enterprise infrastructure and data pipelines at Dynamo Software, earning a company award for ownership beyond his role, and shipped his own products on Steam, Meta Quest, and the App Store, among them a messenger that grew past 900 daily users and an AI coaching app now selling subscriptions. He works across full-stack, mobile, VR, and computer vision, and can tell a technical achievement from a viable business. At Jim, he owns the systems and backend, building and running the platform that keeps its research fast and reliable.
Co-founder, Legal & Compliance
A quantitative finance researcher who works where markets, risk, and machine learning meet. He has held quantitative research and investment roles at Fidelity, building AI and analytical tools for investment management, and at MetaverseMatrix he built portfolio optimization and forecasting systems using linear programming and Monte Carlo methods to improve investment outcomes. He is completing degrees in Electrical Engineering and Computer Science and in Cognitive Science at UC Berkeley, with a focus on Data Science. At Jim, he leads legal and compliance, owning the line between research and advice so every claim it makes on markets, risk, and regulation stays defensible.