In short, we're applying combinatorial auctions to financial markets. Combinatorial auctions are generally speaking computationally intractable. Our art pertains to structuring the problem in a way that's a good fit for finance, while remaining tractable. The classical approach to solving combinatorial auctions for indivisible goods on the "hard" cases (ones without a totally unimodular constraint matrix) generally involves encoding the problem as an integer program, at which point you're faced with solving an NP-complete combinatorial optimization problem. Needless to say, that doesn't lend itself well to financial market speeds or scales. As such, we've taken a different approach based around guided search, similar to AlphaGo.
The Bloomberg article doesn't go into this, but the other half of the model involves inverting the traditional market event loop. As it stands, market participants receive market data and fills from geographically discrepant trading venues, action it via their models, and update their resting orders accordingly. We invert that model by putting points-of-presence equipped with highly precise network timestamping infrastructure at all of the major financial data centers. We use said infrastructure to timestamp snippets of code (our bidding language) that market participants send to us, as well as market data from other exchanges. At discrete, random intervals on the order of every ~100ms we resequence data in accordance with its origin timestamp (buffering by max time-of-flight) and feed it into our client's code for them to act upon and enter orders into our combinatorial auction. The net effect is an environment that's agnostic to geography, immune to latency arbitrage, and free of the traditional exchange race conditions. That's a huge win for everyone currently stuck in the prisoner's dilemma outcome that results from continuously reinvesting in what's now commoditized low latency trading technology.
Combinatorial auctions aren't a new concept, nor are Lamport clocks/distributed systems primitives based on timestamps, nor is the notion of using code as a proxy bidder (ad exchanges). Our art is around making combinatorial auctions practical for finance, both from an API standpoint, and from a computational tractability one. There's a huge impedance mismatch between what market participants want to do from an economic standpoint (trade on a portfolio level/express contingencies/force atomicity across actions) and what they can currently do with traditional limit order book/auction based venues (simulating the aforementioned by cancel/replacing as quickly as possible). Fixing this will be a big win, and broaden market participation.