In 2004, we made an early bet on the National Stock Exchange of India. The conviction was simple: India deserved electronic trading rails, not a trading floor. NSE's daily cash turnover has since grown nearly 18x. Better rails brought in more investors, and those investors got more sophisticated as the market grew around them.
We've seen that pattern play out in every market we've watched. Lower the friction, and more people show up. Give them better tools, and the market gets smarter with them.
So here's a question. You think a super El Niño is coming this winter. What's the trade? Coffee? Cocoa? Fertilizer stocks? A prediction market on hurricane counts? Which venue? What size? What's the hedge? When do you get out? By the time you've worked through all of it, the move already happened. Most people never place the trade at all.
That's what Catalyst fixes.
Why is the trade still handmade?
Every generation of trading killed exactly one friction. Screens killed the floor. E*Trade killed the phone call to your broker. Robinhood killed the commission. Newer venues killed market hours and opened up asset classes that didn't exist ten years ago.
Nobody killed the hardest one: turning an idea into a position. Ideas are fuzzy, qualitative and cross-asset. Execution is mechanical and venue-specific. At a hedge fund, that gap gets closed by analysts, quants and an execution desk. Everyone else closes it with a spreadsheet, five browser tabs and hope.
We see this gap up close. As a crossover firm, Premji Invest runs a public markets team alongside our private investing practice. Every day, that team does the work Catalyst is automating: turning a view on the world into positions, sizing them, and watching for the signal that says the thesis has changed. That gave us an unusual lens in diligence. We weren't just evaluating Catalyst as software. We were asking whether it could do the job our own investors do, and where it would break.
What is Catalyst?
Catalyst is the execution desk most people never had. You type what you believe, literally "El Niño, what's the trade?", and Catalyst's agents research the idea, pick the instruments and venues that best express it, and hand you a strategy you can read before anything runs.
And it doesn't stop at the order. Each strategy is a standing workflow that watches the market for you. If a major outlet reports X, take the position. If another contradicts it, unwind. Take profit here, stop-loss there. Prediction markets, equities, options, perps, sentiment, fund flows and news, all in one interface.
Why not just let the AI trade?
This is the part of the company we spent the most time on in diligence.
Most AI trading products we've seen put the model in the loop. Every time the market moves, the LLM looks at the state of the world and decides what to do next. That's fine in a demo and a problem with real money. Inference on every tick gets expensive fast. Models still fumble a small share of tool calls, and on a $100K account even a rare mistake is a lawsuit. And when the agent sells something at 3am, there's no clean way to reconstruct why.
The fallback most teams reach for is a library of canned strategies. Safe, but nobody's actual thesis fits in a template.
Catalyst does something different. The model writes the strategy, and then it's done. When you create a strategy, the LLM turns your idea into a program, a fixed set of signals, thresholds, venues and actions, that you can read before it ever touches your money. From then on, that program runs on its own. Dylan's framing is that models are great at writing code, so you should treat every agent like a script.
That choice drives the economics too. Catalyst pays for intelligence once, when a strategy is written, not every time it trades. Cost grows with the number of ideas on the platform, not with volume. Today, the inference bill for the whole platform rounds to zero.
Whose edge is it?
There's a second problem with most AI trading tools, and it's a bigger one. LLMs are built to give the most likely answer and to agree with whoever is asking. That's the opposite of what a trader needs. If a million people ask a chatbot for the El Niño trade and get the same answer, that trade is crowded before anyone places it. Consensus isn't alpha.
Catalyst starts from the other end. The thesis is yours. Catalyst's job is to sharpen it and carry it out, not to swap it for the internet's average opinion. It puts the data in front of you, from prediction market odds to positioning, options flow and sentiment, so you can see what the market already believes and whether your view is actually differentiated. Then it executes what you decided, not what the model would have preferred.
The best traders we know don't want an oracle. They want their own judgment, executed faster and more precisely than they could manage by hand.
What's the catch?
If this were easy, every brokerage would have shipped it already. Three things make it hard.
First, data. There are maybe twenty core actions in all of trading: long, short, swap, hedge, rebalance. Strategies don't win on exotic execution. They win on signal: order-book depth, an obscure news thread, crop yields, satellite imagery. Catalyst pays for that long tail and wraps it in its own query language so agents can compose across all of it. Every strategy users build teaches Catalyst which signals actually matter. That's the moat, and it compounds.
Second, trust. Nobody hands their money to a black box. Catalyst never takes custody. Keys sit inside hardware-isolated enclaves that Catalyst's own code can't read, and every trade executes at an established third-party venue or broker. Catalyst is software, not a broker.
Third, incumbents. Big brokerages could build agentic features. But a neutral layer that routes across dozens of venues and asset classes is genuinely hard for any single platform to build. In Catalyst's architecture, every venue is just a swappable adapter. Our bet is that a lot of incumbents end up as distribution partners, not competitors.
Our thesis
The consensus take is that AI in trading is a feature. Incumbents bolt a chatbot onto the app and call it a day. We think that's wrong.
Interface shifts crown new winners. The floor brokers didn't build E*Trade. E*Trade didn't build Robinhood. Every time the interface changed, the company that defined the new one won the next generation of traders. The agentic interface will be no different, and it'll be won by whoever makes AI trustworthy enough to touch your money.
The twist is that Catalyst doesn't have to beat the venues to win. It routes to them. Twenty years ago, we backed the rails. Now we're backing the interface that sits on top of all of them.
The team, and the signal
This is one of the spikiest teams we've met. Dylan Iskandar was an AMP Scholar at Jane Street, built and ran his own trading operation before he finished high school, and did formal verification research at Stanford before dropping out to start Catalyst. He's lived the exact workflow he's automating, and his obsession with provable, deterministic systems shows up everywhere in the product. JZ (Justin Zheng) worked on the early protocol that became Worldcoin as a teenager and is one of the most connected operators of his generation in the Valley. The two met through Tyler Cowen's Emergent Ventures when Dylan was in tenth grade. The rest of the team is Math Olympiad and quant-firm alumni.
In Closing
Every shift in trading removed one layer of friction. Catalyst is going after the last and hardest one: building the trade itself. We're thrilled to back Dylan, JZ and the team alongside Sequoia, Jump Trading, Selini Capital and a bench of traders and AI researchers who are also some of Catalyst's earliest users. Onwards.
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