HorneSci builds math for changing systems.

We are pre-revenue with no customers yet, and we would rather state that plainly than imply otherwise.

HST is a delta-aware sparse runtime, distributed as a licensed in-process library. When a fixed sparse operator is applied to a state that changes incrementally, it computes the new result by touching only what the change reaches. The answer is exact against full recompute, not an approximation.

An engagement starts with a workload trace rather than your source code — operator dimensions and structure, which columns go dirty each step, how clustered they are, and how stable that set is across steps. That is enough to say yes or no, and saying no cheaply is deliberate: a bad-fit deployment is not a small win, it is a pessimization.

From there it is a fixed-fee integration against your operator shape, then a per-deployment runtime license. The tuning is real engineering, so it is named and priced rather than given away as pre-sales labor.

The goal is faster sparse-update systems with evidence a customer can inspect.

HST should make the route visible: what changed, what was reused, when exact fallback was selected, and why the result can be trusted.

In practice that means our evaluation tooling is built to recommend against us when the workload does not fit, we publish the cases where the method loses next to the ones where it wins, and every performance figure on this site traces to a committed benchmark. Where we have only a structural argument and no measurement, we label it that way.

That is the standard for the company as much as the runtime.

The six-question fit test takes about twenty minutes, needs no code changes, and most workloads fail it. That is the expected outcome and a complete answer. If yours passes, or you want a second opinion on a marginal one, write to admin@hornesci.com.