01
Assessment
We take your workload and tell you what it could run at.
Same hardware. More work.
HorneSci is a performance engineering firm. Machines that run around the clock spend most of that time redoing work they have already done. We find it, remove it, and measure what it was worth on the hardware you already own.
Measured, one node
Throughput
15–21×More work completed on the same machine.
Processor load
−38%Headroom returned to everything else running.
Memory
−61%Resident footprint after the change.
Result
ExactBit-for-bit against the original computation.
One machine, one workload, before and after. Measured on an Intel Core i7 node, 2026-07-18. Read this as a deployment result rather than a result about our method: six things changed between the two runs and five of them are architecture — how the work is split, moved and stored — with only the final combining step ours. We did not run the third arm that would separate the two, so our own share of the 15× is not established here. The measurements, as recorded.
The situation
Your machines are already fast enough.
A screening loop that runs every few seconds usually recomputes a result that barely moved. Over a day that is millions of repetitions of an answer the machine had a moment earlier. Buying more machines raises the ceiling without touching the cause.
We go after the repetition rather than the hardware budget.
What we do
What we sell.
02
Deployment
Our engineer works inside your team until it does.
03
Techniques
Our own methods, embedded in your system. The library is free to use and to pass on.
04
Research
We publish what we learn.
Selected results
What changed, and by how much.
Sensor hierarchy rollup
15–21×Readings rolling up through fixed groups, run continuously, on a published streaming benchmark. More throughput on the same node, with lower processor load and a smaller memory footprint.
Camera analytics
2.8–17.6×Fixed-camera video analysis. Measured on real streams, with the heavier per-region work seeing the larger gain.
Sensor fusion
9.9×An industrial identification stream fused in real time. A second reader type on the same pipeline measured 6.8×.
Where it does not apply
One method is narrow. The work is not.
The technique behind the figures above needs six conditions to hold together, and a seventh question decides whether it is worth doing at all. When they do not hold, it is slower than leaving your system alone: roughly half the speed of a good existing implementation when nothing moves between cycles. That is expected behaviour rather than a defect, and we would rather tell you before an invoice than after one.
So we check for it early, against your own code, in about five minutes. A no there rules out that one method and not the engagement.
Tell us what your system recomputes.
What it does, how often it runs, how long that takes today, and what it runs on. Four rough answers are enough to start.