Every general solution makes assumptions
your workload doesn't share.

We take a performance-critical path, map it to your hardware target, and build something that discards every assumption that doesn't apply to your specific problem. The result goes faster than any general library — including the ones we built. The benchmark is the proof, and we'll show you the gap before you commit to anything.

The Proof Of Concept

Jsonifier — our open-source JSON library — wins … out of … benchmark categories against simdjson and Glaze across five platform and compiler combinations. It handles arbitrary key order, arbitrary field sets, arbitrary document sizes, because it has to. A pipeline built for your schema, on your hardware, discards all of that and goes straight to the silicon for exactly one problem. We know this is true because we've done it. The same principle holds for any hot path you hand us — JSON parsing is just the case we can point to with a live benchmark harness behind it.

The Track Record

… – … – …

Jsonifier wins – ties – losses
across 5 platforms

Jsonifier is our open-source JSON parser and serializer — built the same way we build everything: identify what moves to compile time, what moves to initialization time, then shape the algorithm to the hardware. That record was built against simdjson and Glaze on Windows, Linux, and macOS across MSVC, GCC, and Clang.

Every result is fetched live from the repository, including the cases where Jsonifier loses. That's the standard we hold our consulting work to — reproducible, cross-compiler, on your machine, no cherry-picking.

See the full benchmark results →

Client Case Study

Insapient — Quantitative Trading

Augmented Dickey-Fuller test: Python to C++ port

63.7 s

Python runtime (arch 8.0, 32 processes)

288 ms

Ported C++ runtime (32 threads)

3.2×10⁻¹²

Max output deviation

Insapient's stationarity testing pipeline leaned on a Python-based Augmented Dickey-Fuller implementation — the Engle-Granger cointegration test — that had become a bottleneck when run across every ordered pair in the S&P 500: 216,690 pairs over ten years of daily prices (October 2016 to October 2026), standing between the desk and same-day iteration on new signals.

We ported the implementation to C++, mapping the statistical routine to the hardware instead of leaning on general-purpose numerical libraries — the same methodology behind Jsonifier, applied to a different hot path. To give Python its best shot, we benchmarked against the fastest off-the-shelf implementation we could find, engle_granger from arch 8.0 (about 8× faster than statsmodels), spread across 32 processes. It took 63.7 seconds for all 216,690 pairs; our port took 288 milliseconds on the same 32 hardware threads — roughly 220× faster, consistently across Clang, GCC and MSVC builds, with every test statistic matching the arch reference to within 3.2×10⁻¹².

No change in what the test measures — only in how long it takes to find out.

How It Works

Codebase Review

Send us the hot paths. We profile them, map them to your hardware target, and identify exactly where performance is being left on the table — with a concrete, quantified estimate of the gap. This is a fixed-fee engagement that stands on its own.

Implementation

We close the gap on a branch you review and merge, held to your existing test suite — every test green in your CI, with new coverage for new code. Where a hot path is thinly covered, characterization tests come first. There is no optimizing what isn't pinned down.

Proof Delivered

You get the branch and the benchmark harness behind it: behavior verified by your tests, speed verified by reproducible measurement on your hardware. The benchmark is the deliverable, not a slide deck.

You give us read access to the code and its tests. Once we agree on a win worth making, we scope it against the measured gain and implement. Even when a review finds your hot path is already near-optimal, that is a real, billable finding: a documented, benchmarked confirmation that the ceiling is where you thought it was — and where the remaining room actually is.

Scope & Pricing

A Codebase Review is a fixed-fee engagement. You get a profiled, hardware-mapped report on where the performance is hiding and a quantified estimate of the gap. No obligation to proceed.

Implementation is scoped per project against the measured win identified in the review, so the work is sized to the value before it starts. Tell us the hot path and the hardware and we'll come back with a concrete scope and figure.

Who This Is For

Teams with C++ systems hitting a performance ceiling on a hot path — any workload, any domain.
Engineers who already tried the obvious wins and reached for the trusted library — and still need more.
Anyone who suspects the general-purpose solution is leaving performance on the table for their specific data and hardware.
Anyone who treats "this is as fast as it gets" as a question rather than an answer.