Today's AI predicts the future. Intrica computes the best move within it — in the live markets of finance and energy, orders of magnitude faster than simulation, on classical hardware.
We're building the engine that makes the world's hardest problems computable.
Every system that runs the world is governed by uncertainty too vast for any computer to resolve — so for a century, we approximated, simulated, and hoped. Intrica changes what is computable: a single engine, derived from physics, that doesn't just predict the future but computes the optimal decision within it. Starting in the markets that move the world's capital and energy — pricing derivatives, hedging books, dispatching power — problems once beyond reach become ones we can finally compute.
The two waves that remade AI share one engine — prediction — first of language, then of the physical world. The next is different in kind: computing the optimal action under uncertainty.
Predict the next token, trained on the world's text.
Predict the next state of the world, from video and simulation.
Decide the optimal action — weighing the full distribution of futures, not a sample of it.
The space of possible futures explodes exponentially. Predicting it is not the same as knowing what to do — that is the gap.
One engine for wherever high-dimensional uncertainty meets real money — where the gap between a defensible answer and the optimal one is measured in capital, risk, and P&L. Benchmarked in finance.
Pricing, risk, and hedging under correlated uncertainty — fast enough to run intraday, so a desk can act on the numbers while the market is still open.
Dispatch, battery energy storage, and trading across volatile markets — where the optimal call, made in time, is the difference between capturing a price swing and being caught by it.
Pricing derivatives under correlated uncertainty is the hardest decision problem in finance — and the canonical stress test for high-dimensional compute, where Monte Carlo has set the standard for twenty years.
Draws millions of random scenarios and averages them into an estimate — accuracy improving only as 1/√N, every run seed-dependent.
Holds the full range of outcomes in one structured object — solved once, deterministically: the same inputs always return the same answer.
Intrica’s engine fuses two breakthroughs: modern AI, which learns the structure of messy real-world systems no one could specify by hand, and the mathematics of quantum physics, which computes over state spaces too vast to enumerate. The result is tensor network models.
Compute probabilistic tokens. Trained on data.
Compute exponentially large states. Derived from equations.
Paid pilots underway in finance and energy, and direct lines into the institutions at the frontier of European quantum technologies.
Paid proofs-of-concept underway with Tier-1 customers in both sectors.
The transformer scaling era is ending, tensor networks have crossed from research into production, and an entire class of high-stakes decision problems is still out of reach for today's tools.
Frontier training runs approach $5–10B. The next 10× of AI won't come from bigger transformers — it needs new primitives.
Thirty years of physics matured the method. Turning it into a production engine for live markets is the hard part — and takes algorithms and expertise almost no one has.
Prediction has a hundred companies; computing the optimal decision — accurately, auditably — has almost none. Under the Fundamental Review of the Trading Book (FRTB), market-risk capital at top US banks rises $100B → $138B.
Compute. Predict. Decide.
Built in Geneva, Switzerland.