Tell the computer what you want. Essence works out how.
Four short diagrams show what Essence does and why it is different. Use the buttons beside each diagram to step through it. No technical background is needed.
01The big idea
Three ways to get a computer to do something
Every system answers one question: how does a wish become an action? There are three approaches, and they differ in how the route is chosen.
Each step shows one of three approaches, drawn as routes from the same start to the same goal.
Programmers write exact instructions for one situation. It is predictable, but it is only one way to do the thing. When the situation changes, people rewrite it.
Generative AI picks among many likely routes. It is flexible, but it can arrive somewhere nobody intended, and the path is hard to explain afterward.
A Meaning Coordinate names where you want to end up, the way a map coordinate names a place. It can be reached from many starting points, and the result is deterministic. You choose what matters most, such as time, cost, energy or quality, without writing code.
Traditional security guards the perimeter. Essence checks purpose, before and during every run.
A request moves through declaration, validation and monitored execution, with policy-defined responses to deviation.
It states who is asking, what it will do, when, where, how and why, before it can start. Nothing runs unless purpose and authority are declared and validated.
SecuriSync decides whether the request may run, based on identity, integrity and authority. Sensitive workloads can require policy approval first.
While it runs, behavior is compared with what was declared. Revalidation repeats on timelines the customer sets, so trust is not a one-time event.
Policy decides the action: alert, block, halt or remediate, including rolling back to the last validated state. The effect resembles an immune system built into the artifact. Overriding a plan is subject to contextual governance.
Validated results and earlier internal research are kept separate here. Figures are single-GPU, workload-specific results.
Independently validated
20 to 114x
Faster than the first run of the same job, across compute, rendering and data-processing workloads on a single GPU. Validated by AWS and the Rowan University Digital Engineering Hub, and replicated on OCI and GCP.
up to 99.7%
Energy reduction on compute workloads on a single GPU. Confirmed by AWS.
The gain comes from generating hardware-tuned GPU instructions at run time, not from new hardware. Results are workload-specific. Workloads with more parallelism land toward the top of the range, and serialized or I/O-bound ones toward the bottom.
Foundational research
A high-polygon 3D rendering workload on an unmodified 2011 Mac Pro, using CPU multithreading. Bars are drawn to scale, which is why the second bar is so thin. This is an internal MindAptiv measurement from roughly a decade before the GPU pilots. It was not measured by a third party, and it is shown because it demonstrates the same dynamic-parallelism principle, not as part of the validated results above.
Where things stand
Running today: the GPU optimization build at adaptwithchameleon.com, which generates GPU instructions at run time. File-based output is re-established during the first 30 days of funded milestone work. The OS-hosted runtime, illumin8 and the other Aptivs return when the milestones are complete. Wantware is not software: it is not about writing or generating code as applications. Production-scale deployments and public case studies are forthcoming.
See it in action: a live GPU evaluation tool runs at adaptwithchameleon.com, and the demo videos show recorded demonstrations, including some of the earlier version of Essence. The Demos and Pilots page explains what is live today and what comes next.
In short
Essence lets people say what they want instead of writing step-by-step instructions, adapts to whatever machine is present, and checks the purpose of every action before it runs. Its GPU results have been independently validated on a single GPU.