The fastest way to speed up a thousand jobs is to speed up the one thing they all call.
Payments clearing, pricing, overnight risk — the business logic sits on top of numerical libraries that almost nobody reads and everybody depends on. An analyst writing SQL or a notebook never touches that code. They just wait for it.
Some code is called by everything and read by almost nobody.
Underneath the applications people demo sits a layer that does the actual work — pricing and risk math, serialization, hashing, parsing, compression, indexing. It gets called millions of times a day by systems that have no idea it exists. Optimizing it means making that layer cheaper without changing a single thing it returns, so every caller above it inherits the gain and none of them has to be touched, retested or redeployed.
It is also, usually, the code nobody wants to open. It was written by people who have since left, it is load-bearing, and the engineers who depend on it have neither the time nor the context to go in. Artemis is not there to rewrite it: it proposes small, isolated changes to hot paths, each one benchmarked against the behavior that is already there and each one small enough for a human to review. The architecture you have is the architecture you keep.
Where else this shape shows up
Pricing and risk libraries
Monte Carlo, curve building, the math a bank runs overnight.
Serialization and hashing
Hot paths where microseconds repeat millions of times a day.
Search and indexing
The layer under every query box in the product.
Media and compression
Image, video and audio work, priced per byte and per hour.
Geospatial and simulation
Heavy numerical work behind maps, logistics and forecasting.
In-memory data grids
Shared caches and stores every service in the estate talks to.
QuantLib is the open-source library much of the industry prices against. A nested loop in it recomputed the same annuities on every pass; seven lines changed, the unit test runs about 30% faster, and the change is merged.
One overnight risk run, on the library everything above it calls.
- load curves
- simulate
- price book
- aggregate
- report
- load curves
- simulate
- price book
- aggregate
- report
Compute hours
200 h→164 h
Code above it
unchanged
Numbers produced
identical
Paced on the 22% Artemis found in a leading bank's quant library — the cluster was at 95% capacity, so this was headroom recovered rather than bought.
One library. Every job above it.
A quant library is the bottom of a tall stack. Nothing above it has to change for the whole stack to get faster — which is why this is usually the cheapest place to look, and usually the last place anyone does.
Fourteen hundred hours a night is not an abstraction: it is either a batch window that finishes before the desks open, or a cluster you do not have to grow.
What it looks like in public.
A nested loop recomputed the same annuities on every pass. Seven lines changed; the unit test runs about 30% faster. Merged into the library the industry actually uses.
A leading African bank
Its Monte Carlo and risk library ran 22% faster after Artemis. The cluster was already at 95% capacity, so that was headroom recovered rather than hardware bought.
A leading global trading firm
Hashing and serialization on a hot path — the code underneath everything else, where a few microseconds repeat millions of times a day and nothing above it has to be touched.
Discover the hours hiding in your library
Point Artemis at the code every job already calls, and see the improvement it finds, validated, before you change a thing.