AUTONOMOUS-FOX
Our Mission
Deep learning transformed language, vision, and biological sciences. In each case, the breakthrough was the same: learning reusable representations that many downstream applications could build on. No equivalent exists for financial markets.
We are building one. A general-purpose modelling layer that continuously learns how assets behave, individually and in relation to one another, as market conditions evolve. One model whose representations serve trading, execution, risk, and market making simultaneously. Where new applications draw on shared understanding rather than rebuilding it from scratch, and adaptation happens at the level of the model, not through constant reconstruction.
This is what shifts the equation. Today, trading systems scale by adding complexity. We are building the infrastructure that lets capability scale faster than complexity.
Why This Matters
Most quantitative trading operations scale linearly: each new strategy requires its own modelling effort, and complexity grows faster than performance. A foundational market model scales differently, because the same underlying representations can support systematic trading, market making, execution, and risk infrastructure simultaneously, with value compounding as more applications draw on shared research rather than rebuilding it.
This form of leverage requires long-horizon foundational research whose returns emerge across many downstream systems over time. It is structurally difficult to achieve and does not fit naturally within organisations built to improve strategy performance and deploy models into production. We are pursuing it as a primary objective.
Everything begins with behaviour. Language has letters, words, syntax, and grammar. Biology has nucleotide bases, amino acids, and codons. These structural building blocks gave foundation models something to learn from. Financial markets have no such structural building blocks. We are learning them: behavioural states, modes, motifs, and the interactions across a contextual landscape of assets. That is what makes everything else possible.
Structure beats scale. In language and vision, deep learning succeeded through scale. In financial markets, more capacity gives models more rope to memorise noise. What matters here is not how large the model is, but how precisely it controls what it learns, what it preserves as structure, and what it discards as noise.
Depth over breadth. We pursue a small number of hard problems with the attention they require.
Research first. Commercial value follows from genuine scientific progress.
Built to compound. Every advance in the core research strengthens every application built on top of it.
