Measure Before You Scale: SofiaVoice, Cortex-Flow and the Commerce Pipeline

Description and use

RS4 Lab measured and iterated on three systems, cutting latency and adding quality controls before scaling.

Problem

Three AI systems—SofiaVoice, Cortex-Flow, and the Commerce Pipeline—were too slow, unpredictable, or lacked clear metrics, making it hard to decide what to improve before scaling.

Outcome

All three systems achieved deterministic, measurable, and human‑approved pipelines, reducing latency, adding guardrails, and enabling repeatable experiments before scaling.

Workflow

• Define hypothesis for each system
• Instrument all boundaries with metrics and checklists
• Run experiments and collect JSON metrics
• Analyze data and identify bottlenecks
• Iterate on architecture (async service, new TTS, state graph, strict payload)
• Validate with stress tests and human approval
• Document results in Markdown reports

Evidence

Production audit: 5.81 s → 2.80 s (−51.7%)

Source

Route A 8.0 min, Route B 33.9 min, 2/2 routes SUCCESS

Source

84 assertions passing, 0 exceptions, 16–17 items exported per daily run in 5–9 s

Source