I've spent 18 years shipping iOS apps — enterprise VoIP, automotive, retail — and one problem kept resurfacing regardless of the domain: codebases quietly rot, and Swift never had great tooling to catch it early. Most static analysis tools are style linters, not architecture watchdogs. Nothing was really looking for God Objects, tangled responsibilities, or classes that had silently outgrown their purpose.
So I built AIAnalyzer — a Swift CLI, distributed via Homebrew, that does two things: detects real architectural smells using SwiftSyntax, and offers AI-generated fix suggestions through a system that decides, in real time, whether to trust a fast local model or escalate to a stronger cloud one.
The problem with "just use AI for everything"
Bolting an LLM onto a dev tool is easy. Bolting one on responsibly is a different problem. Two extremes are both bad:
Cloud-only: great quality, but every request leaves the machine. For teams under compliance constraints — banks, healthcare, defense — this is a non-starter.
