AI Code Quality & Tech Debt Remediation

The AI-Generated Code Problem Nobody Wants to Talk About

Your team shipped faster than ever last quarter. Copilot, Cursor, Claude — the AI tools delivered. Features went out the door at twice the previous velocity. And now you are staring at a codebase where three different modules solve the same problem in three different ways, error handling is inconsistent across every controller, and nobody can explain why there are four competing patterns for database access.

This is not a hypothetical. We audit codebases for enterprise teams in healthcare, education technology, and manufacturing — and the pattern is identical across every engagement. AI-generated code ships fast, passes basic tests, and quietly accumulates architectural inconsistencies that compound into real technical debt within months.

The issue is not that AI writes bad code. The issue is that AI writes plausible code. It compiles. It works. It passes the PR review because it looks reasonable. But it does not follow your team's conventions, it does not reuse your existing abstractions, and it introduces subtle duplication that makes every future change harder.

"We went from shipping a feature every two weeks to shipping three a week. Six months later, our deployment cycle had doubled because nobody could change anything without breaking something else." — Engineering Director, Series B healthcare SaaS company

What an AI Code Quality Audit Actually Involves

We do not run a linter and hand you a PDF. Our code quality audits are hands-on assessments performed by senior engineers who work daily in TypeScript, Ruby/Rails, and Elixir/Phoenix — the ecosystems where AI-generated code creates the most insidious problems.

Static Analysis with Language-Specific Tooling

Every audit starts with automated static analysis calibrated to your stack:

• Elixir: Credo for consistency and refactoring opportunities, Sobelow for security vulnerabilities, Dialyxir for type specification violations
• Ruby/Rails: RuboCop with custom cop configurations tuned to your team's style, Brakeman for security scanning, Reek for code smell detection
• TypeScript: ESLint with strict mode enforcement, custom rule sets for React/Next.js patterns, TypeScript strict compiler flags (noImplicitAny, strictNullChecks, noUncheckedIndexedAccess)

But static analysis alone misses the structural problems that AI code introduces. The linter does not flag that your codebase now has two different patterns for handling API errors — both technically correct, neither consistent with the other.

Architectural Pattern Analysis

This is where the real work happens. We trace data flows, identify duplicated abstractions, and map the actual architecture against your intended architecture. Specific things we look for:

• Convention drift: Where AI-generated code introduced patterns that conflict with your established conventions
• Abstraction fragmentation: Multiple implementations of the same concern (authentication, error handling, data validation) that should be unified
• Coupling introduced by copy-paste generation: AI tools frequently inline logic that should be extracted into shared modules
• Test coverage gaps: AI-generated code often ships with tests that verify the happy path but ignore edge cases, error states, and integration boundaries
• Security anti-patterns: Hardcoded configurations, overly permissive access controls, and missing input validation that AI tools introduce when they lack context about your security model

Dependency and Supply Chain Review

AI coding assistants suggest dependencies aggressively. We audit your dependency tree for abandoned packages, overlapping functionality between libraries, and packages that introduce unnecessary attack surface. In Elixir projects, we commonly find three or four hex packages performing jobs that the standard library handles natively. In TypeScript projects, we find lodash imported for a single function that exists in modern JavaScript.

Establishing AI Coding Standards That Actually Stick

Audits identify the damage. Standards prevent it from recurring. We work with your engineering team to establish AI coding guidelines that are enforceable through tooling — not just written in a wiki that nobody reads.

Codified Style Enforcement

We configure and deploy language-specific tooling that runs on every commit:

• Pre-commit hooks that catch convention violations before code reaches a pull request
• CI pipeline gates using Credo, RuboCop, ESLint, and Sobelow that block merges when standards are violated
• Custom rules written for your specific codebase — not generic configs pulled from a blog post

AI Prompt Engineering Guidelines

Your developers are going to keep using AI tools. The goal is not to ban them — it is to channel them. We develop prompt templates and context-injection strategies that help AI assistants produce code consistent with your conventions. This includes:

• Repository-specific instruction files that AI tools read automatically
• Architecture decision records (ADRs) formatted for AI consumption
• Pattern libraries that developers reference when prompting AI for new features

Pull Request Review Standards

We help your team develop review checklists specifically designed to catch AI-generated code problems: convention drift, unnecessary abstraction, duplicated logic, and missing error handling. These are not theoretical — they are built from patterns we have observed across dozens of codebases.

Refactoring AI-Generated Code Without Breaking Everything

The hardest part of remediating AI-generated technical debt is that the code works. You cannot justify a rewrite to stakeholders when the application is functioning. Our approach is incremental, test-driven, and designed to deliver measurable improvements without destabilizing your product.

Test-First Remediation

Before we change a single line of production code, we establish a test safety net around the areas we plan to refactor:

• ExUnit for Elixir modules — property-based testing with StreamData where behavior is complex
• RSpec for Ruby/Rails — request specs and model specs that capture current behavior before we modify it
• Vitest for TypeScript — unit tests, integration tests, and snapshot tests that lock in expected behavior

This is not optional. Vibe-coded systems are especially fragile because the original developer (the AI) had no mental model of the system's invariants. Changing one module can break another in ways that are not obvious from reading the code.

Incremental Unification

We consolidate duplicated patterns one at a time. If your codebase has three different approaches to API error handling, we do not rewrite all three in a single PR. We:

• Identify the best existing pattern (or design a new canonical one)
• Migrate one usage at a time, with full test coverage on each migration
• Deprecate the old patterns and add linter rules to prevent reintroduction
• Document the canonical pattern so your team — and their AI tools — use it going forward

Performance and Security Hardening

AI-generated code frequently has performance characteristics that are invisible during development but emerge under production load. N+1 queries in Rails, unbounded process spawning in Elixir, unoptimized React re-renders in TypeScript frontends — these are the patterns we identify and remediate during refactoring.

Security hardening follows the same incremental approach. We address findings from Sobelow, Brakeman, and manual review in priority order: critical vulnerabilities first, then medium-severity issues, then hardening measures that reduce future attack surface.

Automated Quality Gates for Ongoing Protection

The remediation engagement ends, but the tooling stays. Every project we deliver includes a fully configured CI/CD quality gate pipeline that your team owns and maintains:

• Elixir projects: mix credo --strict, mix sobelow, mix dialyzer, and mix test --cover with minimum coverage thresholds
• Ruby/Rails projects: rubocop, brakeman --no-pager, bundle audit, and rspec with coverage enforcement via SimpleCov
• TypeScript projects: eslint --max-warnings 0, tsc --noEmit with strict flags, and vitest run --coverage with branch and statement thresholds

These gates are not suggestions. They block merges. If a developer — or their AI assistant — introduces code that violates your established standards, the pipeline catches it before it reaches your main branch.

Why Enterprise Teams Choose Of Ash and Fire

We are not a generic consultancy that Googles your framework while billing you hourly. Our engineers write production Elixir, Ruby, and TypeScript every day. We have deep experience in regulated industries — healthcare applications with HIPAA requirements, educational platforms with FERPA obligations, manufacturing systems with real-time reliability demands.

• Multi-ecosystem fluency: Most shops specialize in one language. We work across TypeScript, Ruby/Rails, and Elixir/Phoenix because enterprise teams rarely have a single-language codebase. Your AI code quality problem spans your entire stack, and so does our remediation work.
• Compliance awareness: In healthcare and education, code quality is not just an engineering concern — it is a regulatory one. We understand what auditors look for and build quality gates that satisfy both engineering standards and compliance requirements.
• Knowledge transfer: We do not create dependency on our team. Every engagement includes pairing sessions, documentation, and training so your engineers can maintain the standards and tooling we establish.

Start With a Code Quality Assessment

If your team has been shipping with AI assistance for six months or more, you have technical debt accumulating that your current review process is not catching. The longer it compounds, the more expensive it becomes to remediate.

We offer a focused two-week code quality assessment that gives you a complete picture: what is working, what is drifting, and exactly what it will take to bring your codebase back to a maintainable state. No generic reports — specific findings, prioritized recommendations, and actionable remediation plans.

Contact us to schedule your AI code quality assessment.