Give Us One Bounded Workload.
Measure. Add ExergyNet. Measure Again.
This is an evaluation methodology, not a sales pitch. We measure your current, already-optimized architecture first — then integrate only the ExergyNet layer relevant to your workload, and measure again. Both positive and negative outcomes are reported.
Benchmark the workload. Don't believe the claim.
Every public number on this site is bounded to the envelope it was tested in — a specific hardware target, a specific corpus, a specific baseline. That discipline is exactly why those numbers cannot tell you what ExergyNet will do for your workload, on your infrastructure, against your already-optimized system. The only way to know that is to measure it. This page is the process for doing that.
Six steps. Nothing assumed.
Define workload
Name a specific, bounded, state-heavy or multi-step task your organization actually runs today.
Freeze success criteria
Agree in advance what counts as a qualified successful task — before any number is measured.
Measure current architecture
Baseline your existing, already-optimized system on the frozen criteria. No ExergyNet involved yet.
Integrate only what's needed
Add the specific ExergyNet layer relevant to the workload — not the whole architecture by default.
Measure again
Re-run the same frozen criteria against the same workload with ExergyNet integrated.
Report both outcomes
Positive and negative results are both reported. A workload where ExergyNet doesn't help is a valid, useful outcome.
Cost per qualified successful task, itemized where possible.
Where relevant to the workload, the evaluation can account for:
Not every term is instrumented for every workload today — inference and state overhead are the terms ExergyNet's own published benchmarks currently measure directly (highlighted above). Verification, retries, orchestration, network, and storage costs are accounted for where the specific workload and design partner's own measurement setup make them measurable; this evaluation does not assume they are already solved.
Built to be sent directly to the person who has to sign off.
CTO
Technical ownership of the workload and the decision to integrate.
CIO
Infrastructure and vendor-risk ownership.
Chief AI Officer
Ownership of AI-specific efficiency and governance decisions.
AI Infrastructure Lead
Direct operational ownership of the systems being measured.
Enterprise Architect
Ownership of how the integration fits the broader system landscape.
Keep your models. Keep your cloud.
A design partnership evaluates a bounded workload — it does not require migrating your stack, replacing your model vendor, or transferring institutional authority to ExergyNet. See the full compatibility statement on the homepage for what ExergyNet does and does not require, including the one disclosed exception (Vanguard's optional inference API).
Ready to measure your own workload?
Tell us the workload. We'll scope the evaluation together before anything is integrated.