Find the cost, then remove it.
A plugin that uses four percent of a core is a plugin people put on every track. One that uses fifteen gets used once per session, and then gets replaced. The same is true of a slow desktop app, a costly inference service or a frontend that makes users wait.
We start with a representative workload and a baseline: CPU time, allocations, latency, memory, battery, throughput or frame time. Then we profile the whole path on the hardware your customers actually run, fix the highest-cost bottleneck, and measure again. In C++, Python and Rust, we can work from the audio callback through the application, services, database, frontend and deployment environment. Every change is measured on both sides, so the improvement is a number rather than an impression.
We reproduce the workload that matters: a real session, a large project, a mobile device under sustained load, a production request or a slow browser path. We record the metric that hurts users and establish a repeatable before-number on Apple silicon, x86 or the target device.
In audio we look for denormals, cache misses, work done per sample that belongs per block, allocation on the audio thread and locks in places nobody meant to put one. In apps and services we look for unnecessary copies, serial I/O, oversized payloads, poor query plans, repeated parsing and work performed at the wrong layer.
We restructure the hot path first, then apply SIMD across NEON and AVX, better Python boundaries, allocator and ownership work in Rust, caching, batching or parallelism where the algorithm genuinely allows it. We keep realtime-safe code realtime-safe and preserve the output contract unless you approve a change.
We rerun the baseline, compare the before and after figures, check output quality and document the trade-offs. Then we put the benchmark in your CI so a regression shows up as a failed build rather than as a customer report six months later.
It depends entirely on the starting point. Codebases that have never been profiled often have a two to five times gain sitting in one or two functions. Tuned code is a harder and more incremental job.
Only if you agree to it. We aim for bit-exact output, and where a change alters it we say so and quantify it.
Yes, including NEON paths and the memory behaviour that differs from x86.