HorneSci

Reduce token consumption and energy use by eliminating redundant calculations and reusing what’s already computed.

Simulations, solvers, and monitoring systems often apply the same matrix, grid, or network to an input that changes a little at a time, and still recompute the whole result every step. We change that code to update the result from the part of the input that changed, and we measure the new version against the old one on your hardware.

Where the work repeats

Simulation

Simulations that revisit the same system while only a small region of the grid changes.

Rows of consoles in Mission Control, Houston, in 1965, with flight controllers at work.

Monitoring

A state estimate re-solved over a fixed network each time a few sensors report.

A long row of supercomputer racks with perforated doors, seen at an angle.

Data centers

Rollups from sensor to rack to room, rebuilt from the leaves when a few readings move.

Fixed cameras

Video analysis where most of each frame matches the frame before.

Contact us

  1. Call

    30 minutes

    We ask what the system does, how often it recomputes and how long that takes, what it runs on, and how exact the result has to be. No preparation needed.

  2. Assessment

    2–4 weeks, fixed fee

    We run your workload on your machines and report what we measured, the conditions behind each number, and the changes that would produce it.

  3. Deployment

    In your codebase

    We make the change inside your program, in its own language, with your engineers.

We work under your NDA and on your machines, connected or air-gapped. Your data stays with you, and we publish only what you approve in writing.

A 1963 computer room: tape drives along the wall, a woman loading a reel, a printer and an operator in the foreground.

Delta Routing

Delta Routing, our public project, updates the output of a fixed operator from the part of its input that changed.

75.06×
as fast as recomputing, with 1% of the input changed
3.94×
as fast, with 16% changed

Update from the change against a full matrix-vector product (cblas_dgemv, tuned OpenBLAS 0.3.26, one thread). N = M = 1024, f64, scattered changes, one pinned Intel i7-1165G7 core. Measured 11 August 2026.

More results at deltarouting.com

About

HorneSci is a software and research company with two cofounders.

About HorneSci
Portrait of Hunter Horne.

Hunter Horne

Cofounder, mathematics and theory

LinkedIn
Portrait of Eric Spencer.

Eric Spencer

Cofounder, implementation

LinkedIn

Member of the NVIDIA Inception program and AWS Activate.

  • HorneSci is a member of the NVIDIA Inception program.
  • Powered by AWS. HorneSci is a member of AWS Activate.