AI-Native iOS App Architecture Checklist (20 Points)
A practical pre-build and pre-launch checklist for teams shipping AI-native iOS products. Use it to reduce avoidable rewrites, privacy risk, and performance regressions.
Think of this checklist like a structural survey before construction. It helps you catch design risks early, when fixes are cheap and fast.
Product and Scope
Quick summary: Make clear decisions now so the product stays stable as usage grows.
- Define one primary user job this AI feature must solve.
- Write one clear success metric for launch (for example: task time reduced by 30%).
- List where AI is required vs where a simple rule is enough.
- Create a fallback UX for unsupported devices or low-confidence output.
On-Device AI Foundations
Quick summary: Make clear decisions now so the product stays stable as usage grows.
- Choose Core ML vs Foundation Models intentionally and document why.
- Set computeUnits strategy (latency-sensitive paths should prefer Neural Engine).
- Decide cold-start behavior: preload, lazy load, or hybrid.
- Define a confidence threshold and low-confidence handling path.
Data and Privacy
Quick summary: Make clear decisions now so the product stays stable as usage grows.
- Design local-first storage before feature coding starts.
- Define which AI outputs are persisted and for how long.
- Add data minimization rules for prompts, logs, and analytics events.
- Align app behavior with App Privacy disclosures before submission.
Performance and Reliability
Quick summary: Make clear decisions now so the product stays stable as usage grows.
- Benchmark inference latency on your lowest supported device.
- Track memory usage under sustained inference load.
- Add thermal-state handling to avoid performance collapse.
- Move heavy, non-urgent AI work to background task windows.
Architecture and Delivery
Quick summary: Make clear decisions now so the product stays stable as usage grows.
- Isolate ML runtime behind actor-based service boundaries.
- Use deterministic input/output contracts for every AI entry point.
- Define model update and rollback strategy before release.
- Run App Store readiness checks for entitlements, privacy, and edge cases.