Kimpton

In investment management, every decision gets graded. The market prints the answer on a date known in advance, and nobody gets to argue with it.

Kimpton is financial AI that answers to reality. Kimpton OS is the AI-native operating system for investment teams: it does the research and hands PMs structured trades. EarningsBench is a public benchmark where models forecast quarterly earnings before companies report and the reported numbers do the scoring. Koliseum is the set of point-in-time financial environments we build from that work, for labs to train and evaluate models on.

The whole industry is fighting underperformance. Active managers trail their benchmarks, and capital keeps bleeding into passive. AI was supposed to fix that, but it hasn't. Funds either stitch together a dozen point tools or babysit general agents all day, and neither gets them to the trade.

Kimpton OS goes straight to it. It ingests a fund's mandate, strategy, and positions, connects to the institutional data the fund already runs on, and returns a structured trade proposal built for that fund. It's built to disagree, because consensus is poison for investment decisions. Instead of telling a PM what they want to hear, it surfaces the breaches and risks they'd otherwise miss.

That same work is how we grade AI. Static benchmarks leak into training data. Simulations reward what their authors imagined. A forecast sealed before the outcome exists and scored when the company reports can't be rehearsed or backdated. We started in markets because they're the one place reality answers on schedule.

We're two ex-Goldman engineers and an ex-SpaceX engineer. We left to start our own quantitative systematic hedge fund, raised $10M from a family office at 21 and 22, and scaled it to $35M over four years. We learned to trust live results over historical fit. We built Kimpton inside that fund, then productized the stack.

We're live with our first institutional client, a $3.3B manager. Our first market is fundamental equity funds from $1B to $10B, and the labs that need to know whether their models can do this work.