Growth Systems
Growth doesn’t just increase traffic —it changes system behavior.
When products scale, they often feel slower before anything looks broken. This hub is a practical map: what users feel first, what’s actually happening under the hood, and how to fix it with evidence — not guesswork.
Legacy support content. For active AI services, start with LLM Audit or Latency & Serving.
Constraint-first
Find what the system is waiting on.
Distributions
p95/p99 reflects user reality.
Evidence-based
Baseline → isolate → fix → validate.
Legacy reading
Archive guide from the earlier system-engineering content set. Use it as background, then route decisions through AI Audit and Optimization Sprint.
What people notice first
These are the early warning signs most teams miss — until users complain.
Average Latency Is Lying
Averages can look fine while users complain. Growth pain shows up in the tail.
P95/P99 Gets Worse First
Tail latency is the earliest signal your system is becoming constrained.
Timeouts Are a Scaling Signal
Timeouts rarely happen randomly. They show up when a system starts waiting — queueing, saturation, dependency variance, or retry amplification.
AI systems & growth
LLM cost spiking as usage grows? We baseline cost per conversation, optimize routing and caching, and prove ROI with before/after benchmarks.
Cost Optimization
Token, context, routing, ROI, unit economics for LLMs.
LLM Audit
Audit framework, baselines, troubleshooting for LLM/RAG.
AI Production Audit
Baseline cost/conv, quality, speed. Get prioritized plan.
Optimization Sprint
Fix accuracy, cost, latency—with measurable outcomes.
Ready to improve production performance?
Pick a starting point. We’ll keep it focused and measurable.