Why this exists

Latency discussions are dominated by intuition, confidence and post hoc profiling. By the time a real system can be measured, the decisions about concurrency, sequencing and delay have hardened and are expensive to undo. Profilers explain where the time went; they rarely explain why the user waited.

Simquence moves the question to design time. Everything is named in the model and nothing is inferred; scheduling is deterministic and randomness is seeded. When the numbers change, it is because the model changed, never because the measurement drifted.

The results routinely betray intuition. In the shipped checkout model most people predict the slowest back end dominates; instead a short debounce, added so the interface would feel calm, is the critical path in most runs. The same engine produced the hierarchy-depth and authority-placement numbers in Relativistic Decision Architecture from published models and seeds, so every quantitative claim there can be rerun.

It is a local Python CLI with a desktop UI over the same headless core: keyboard navigable, 100% coverage gated, no account and no server. Your models never leave your machine; the only outbound call is an anonymous daily check of GitHub for a newer release. It exists so the latency problem is found before it is politically expensive.