What's included
dylib load time, static initialiser audit, and main-thread work reduction. Target: <400ms cold launch.
Heap profiling, retain cycle detection, memory growth over session lifetime. Eliminates OOM crashes.
Request waterfall analysis, parallelisation, caching strategy, and payload reduction.
Off-screen rendering, overdraw, list scrolling jank — Instruments Time Profiler + Core Animation analysis.
Production performance telemetry so you know when P95 regresses after the next release.
XCTest baselines for launch time and key user flows — CI fails if performance regresses.
How it works
Establish current benchmarks across launch time, memory, and network. Identify the top 3 high-impact regressions.
Implement fixes one at a time, re-profile after each. No guessing — every change is validated against the baseline.
MetricKit hooks and XCTest performance baselines so the next team member can't accidentally re-introduce the regression.
Is this right for you?
Apps with poor App Store ratings
Performance complaints in reviews are fixable. Profiling almost always reveals 2-3 root causes that explain the majority of complaints.
Teams before a major release
Performance review before you ship to 100k users is cheaper than hotfixing after.
Fintech & enterprise apps
Regulated apps often carry legacy networking and storage patterns that quietly kill performance.
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