Optimal rebalancing plus full risk attribution inside a hard latency budget — at universe sizes where standard optimizers stall. The decision isn't useful if it arrives after the moment to act has passed. PRISM returns an optimal rebalance, a factor/specific risk breakdown, and an audit trail — well under a second, across tens of thousands of holdings.
A rebalance you can't explain is a liability, and one that misses the latency budget is just history. The job is to return the optimal trades and the risk attribution together, inside the budget, at a universe size where standard optimizers stall — not to pick one of the three.
Real-time portfolio decisions carry two demands at once: produce the optimal rebalance, and produce the risk attribution that justifies it — both inside a latency budget set by the desk, not the solver. A great rebalance with no risk breakdown can't be signed off; a fast number that arrives after the window is closed can't be acted on.
Now scale it. At tens of thousands of holdings, the universe itself becomes the obstacle: the work grows faster than the book, and standard optimizers stall exactly where the decision matters most. The constraint is doing all of it — optimal, attributed, auditable — at scale, in time.
Any one of these is routine. All of them together, at universe scale, inside a latency budget, is where conventional approaches run out of road.
At tens of thousands of holdings the universe grows faster than the book, and standard optimizers stall where it matters most.
The rebalance and its factor/specific attribution have to come out of the same run, not a slow second pass that arrives too late to use.
"Well under a second" is the spec, not the aspiration. A decision outside the latency budget is a decision you can't take.
You bring positions, targets, your risk view, and a latency budget; PRISM returns the trades, the attribution, and the audit trail — inside it. The methods are proprietary; the interface is simple.
One engine, a hard budget, deterministic.
Demonstrated results on the dataset described — not a guarantee. Comparators are referred to generically as conventional / standard solvers.
An optimal rebalance, a factor/specific risk breakdown, and an audit trail returned in well under a second.
A real 75,000-asset universe — across tens of thousands of holdings, where standard optimizers stall.
The risk attribution comes out of the same run as the trades, with a content-hashed audit trail.
A matched-workload pilot on your holdings, risk view, costs, and latency budget — a pass/fail metric you set before we start, every losing case shown.
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