Computational infrastructure for systems that have to work in the real world.

Engineers develop in simulation and deploy to embedded hardware. The solver that worked in the lab usually isn't the solver that ships on the device. SolvSRK is one solver that runs in both places, with identical behavior and no translation step.

Every digital twin today runs different software on the device and in the cloud. They disagree, and no one knows which to trust. SolvSRK lets you run the same physics, on the same solver, on both sides — so divergence means a real fault, not a software artifact.

A controller that can measure its own uncertainty can run tighter when the data is clean and back off when the data is noisy. SolvSRK makes that possible by solving the physics and tracking the uncertainty in one computation.

Quantifying uncertainty in a simulation usually means running it hundreds of times with Monte Carlo. SolvSRK produces calibrated error bars alongside the answer in a single solve — fast enough for real-time control loops and regulatory certification.

Card networks compress transactions down to twenty fields before the issuer decides. We sit on the network, extract ten bounded intelligence signals from the full picture, and deliver them alongside the standard message. Banks running rules engines outperform banks running machine learning — with a tenth of the regulatory exposure.

We built the lab we needed to validate our own solver. It turned out to be a product category: pre-registration, multi-layer quality assurance, AI agent workforce, structured evidence management. One engineer. Six weeks to stand up. Over 1 billion validated data points — the nearest competitor has 30,000. Zero retracted findings.

SolvScout fingerprints your ODE system. SolvTune benchmarks every candidate solver on it and shows you a comparison table. When a competitor wins, the table says so. When our solvers win, the user already saw the evidence on their own problem.

The desktop app that turns solver selection from guesswork into evidence. SolvBench orchestrates Scout, Tune, and the runtime — characterize your problem, benchmark the candidates, apply the winning config, and watch the solver work in real time.

Every options pricing library solves the Black-Scholes PDE by marching through hundreds of time steps. LRDE computes the answer as a single contour integral in the Laplace domain. Cost is independent of maturity and stiffness; one LU factorization is reused across every strike, maturity, and scenario. The pricing inner loop gets 5–40× faster while producing the same prices to sub-basis-point precision.

One trillion scalar observations. One billion validated data points — the nearest competitor has thirty thousand. 821 billion function evaluations across 3.5 million instrumented trial runs. Zero retracted findings. Every number traces back to a test, a seed, and a verdict.