AI-generated code cleanup — refactor, dedupe, type, test.
Turn the Cursor, Claude Code, Bolt, or Lovable codebase you shipped fast into something a human team can extend. Strict TypeScript, Zod at boundaries, real tests on the money paths, and a rules file that keeps the next prompt on-architecture.
AI-generated code cleanup is a fixed-fee refactor pass for Cursor, Claude Code, Lovable, and Bolt codebases — we deduplicate logic, tighten types to strict, add real tests on the money paths, patch security gaps, and leave a rules file so the next prompt stays on-architecture. Roughly half of AI-generated code ships with known vulnerabilities (industry benchmark; see our 2026 research). Typical engagement is 2 to 4 weeks from $3,999, audit in 48 hours at $1,500 rolled into the engagement, no rewrites.
Symptoms AI-generated code cleanup fixes
Eight failure classes we find on nearly every Cursor, Claude Code, Copilot, Lovable, and Bolt codebase. Each row maps the symptom to the AI-generated pattern and the refactor we ship.
| Symptom | Root cause (AI-generated pattern) | Our fix |
|---|---|---|
| Same helper written three different ways | Cursor / Copilot regenerated the same function per file instead of importing it | Duplication scan, consolidate into shared util, update call sites, add an ESLint rule that catches regressions |
| Runtime errors sneak past TypeScript | AI generator typed parameters as any or unknown and cast aggressively | Enable strict, noUncheckedIndexedAccess, add Zod schemas at every API boundary |
| One React component crashes the whole app | No error boundaries; effects throw synchronously and unmount the root | Route-level error boundaries + Sentry reporting + graceful fallback UI |
| God component with 800 lines and 14 hooks | Claude Code kept appending features into the same file | Extract data-fetching into hooks, split UI into smaller components, add tests as we split |
| Tests mock the exact thing they are supposed to assert | AI stubbed the module under test to silence the failure it should have caught | Replace with real integration tests using MSW or Playwright on the critical paths |
| Three ways to fetch data, four ways to handle errors | Each prompt authored its own pattern; no rules file kept them on-architecture | Pick one pattern, refactor call sites, write a .cursorrules / CLAUDE.md to keep new code compliant |
| API routes with no auth check | AI generator shipped the happy path and forgot the guard | Audit every route, add middleware auth, write a regression test that hits the route as an anon user |
| Silent regressions every time the AI edits | No CI, no type-check gate, no test gate on PRs | GitHub Actions pipeline: tsc, ESLint, integration tests, required to pass before merge |
Cursor code cleanup schedule — audit to handoff
Five steps from the 48-hour written audit through commit-by-commit refactor to architecture handoff.
- D1–D2
48-hour cleanup audit
We read the repo, map the duplication, score the type looseness, list the security gaps, and return a prioritized blocker list. Fixed $1,500 audit fee, rolled into the engagement if you proceed.
- D3
Prioritize by blast radius
We start where the blast radius is biggest — security gaps, data correctness, then duplicated logic that slows feature work. You get a written refactor plan before a single line is touched.
- W1–W2
Refactor AI-generated code in place
Shared utilities consolidated, types tightened, tests added on critical paths, security gaps patched. We commit per file so you can review each change. No big-bang rewrites.
- W2–W3
Add guardrails that outlast us
Project rules files, strict TypeScript settings, lint rules, and a CI pipeline that rejects regressions. When the next Cursor or Claude Code prompt runs, the guardrails hold.
- W4
Handoff with architecture docs
Architecture overview, onboarding doc, and an inventory of every file touched. Your next engineer — or the AI tool that wrote the first draft — can extend the codebase without re-introducing the mess.
- D1–D2
48-hour cleanup audit
We read the repo, map the duplication, score the type looseness, list the security gaps, and return a prioritized blocker list. Fixed $1,500 audit fee, rolled into the engagement if you proceed.
- D3
Prioritize by blast radius
We start where the blast radius is biggest — security gaps, data correctness, then duplicated logic that slows feature work. You get a written refactor plan before a single line is touched.
- W1–W2
Refactor AI-generated code in place
Shared utilities consolidated, types tightened, tests added on critical paths, security gaps patched. We commit per file so you can review each change. No big-bang rewrites.
- W2–W3
Add guardrails that outlast us
Project rules files, strict TypeScript settings, lint rules, and a CI pipeline that rejects regressions. When the next Cursor or Claude Code prompt runs, the guardrails hold.
- W4
Handoff with architecture docs
Architecture overview, onboarding doc, and an inventory of every file touched. Your next engineer — or the AI tool that wrote the first draft — can extend the codebase without re-introducing the mess.
Lovable code refactor — anatomy of one file
The shape we find on Cursor and Claude Code output, and the shape we ship back. Same rendered behavior, 420 lines down to 72, with types and tests that actually assert.
01// components/UserDashboard.tsx — AI-generated (Cursor)02// 420 lines, no types, duplicated fetchers, silent failures03 04export default function UserDashboard(props: any) {05 const [data, setData] = useState<any>(null);06 const [loading, setLoading] = useState(false);07 const [error, setError] = useState<any>(null);08 09 // duplicated fetch — repeated in 3 other components10 useEffect(() => {11 setLoading(true);12 fetch("/api/users/" + props.userId)13 .then((r) => r.json())14 .then((d) => setData(d))15 .catch((e) => setError(e))16 .finally(() => setLoading(false));17 }, [props.userId]);18 19 // mixing fetching, derived state, and rendering in one component20 const totalSpend = data?.orders21 ?.filter((o: any) => o.status === "paid")22 .reduce((a: any, b: any) => a + b.amount, 0);23 24 if (loading) return <div>Loading...</div>;25 if (error) return <div>Error: {error.message}</div>;26 27 return <div>{/* 300 more lines of JSX + inline handlers */}</div>;28}01// components/UserDashboard.tsx — Afterbuild Labs refactor02// 72 lines, typed, reusable hook, error boundary above03 04import { z } from "zod";05import { useUser } from "@/hooks/useUser";06import { DashboardView } from "./DashboardView";07 08const UserSchema = z.object({09 id: z.string().uuid(),10 email: z.string().email(),11 orders: z.array(z.object({12 id: z.string().uuid(),13 status: z.enum(["pending", "paid", "refunded"]),14 amount: z.number().int().nonnegative(),15 })),16});17export type User = z.infer<typeof UserSchema>;18 19export function UserDashboard({ userId }: { userId: string }) {20 // shared, typed, cached across components21 const { data, status } = useUser(userId, UserSchema);22 if (status === "loading") return <DashboardView.Skeleton />;23 if (status === "error") return <DashboardView.Error />;24 return <DashboardView user={data} />;25}What the code cleanup engagement ships
Twelve deliverables fixed into the statement of work. If your codebase needs more — domain refactor, framework migration, database redesign — we scope a separate engagement for it.
- 01Full-codebase duplication scan and consolidated shared utilities
- 02Strict TypeScript pass — remove
any, add generics, add Zod at data boundaries - 03Integration tests on critical paths (auth, payments, core data flows)
- 04Security pass covering unvalidated input, leaked secrets, missing auth checks on API routes
- 05Standardized data-fetching, error-handling, and state patterns across the codebase
- 06Dependency audit — remove duplicated or vulnerable packages
- 07Error boundaries on route-level components so one crash cannot blank the app
- 08CI pipeline (GitHub Actions) running tests, type-check, and lint on every PR
- 09Architecture overview document for onboarding the next engineer
- 10Senior code review on Cursor / Claude Code / Copilot output for the next 30 days
- 11Project rules file (
.cursorrules/CLAUDE.md/.windsurfrules) codifying conventions - 12Delivery commit-by-commit so you can review each change before merge
Fixed-fee AI-generated code cleanup tiers
- turnaround
- 2–4 weeks
- scope
- Mid-sized single-app codebase · refactor, types, tests, CI
- guarantee
- Commit-by-commit delivery, reviewable at any point
vs hourly refactor · vs a Bolt code to production-ready rewrite
Why the fixed-fee, commit-by-commit cleanup beats the alternatives on AI-generated code.
| Dimension | Hourly contractor | Afterbuild Labs cleanup |
|---|---|---|
| Pricing model | $150–$250/hr, open-ended | Fixed fee from $3,999 after $1,500 audit |
| Approach | Often pushes a full rewrite | Refactor in place; rewrites are a last resort we tell you about |
| AI-specific failures | Treats the codebase like any other legacy project | Knows the Cursor, Claude Code, Lovable, Bolt, Copilot patterns by shape |
| Guardrails after handoff | Usually none — regressions return on next prompt | Rules file + strict TS + CI pipeline that rejects regressions |
| Deliverable | New codebase, no written architecture doc | Commit-by-commit refactor + architecture overview + 30 days AI-PR review |
Who the code cleanup service is for
Pick the cleanup if…
- →You shipped fast with Cursor, Claude Code, Copilot, Windsurf, Bolt, or Lovable and every new feature takes longer than the last
- →Your investors or acquirer's due-diligence team are about to read the code
- →You are handing off to a full-time engineer and do not want them to quote a rewrite on day one
- →Your team is generating code faster than you can review it, and silent regressions keep shipping
- →Types are loose enough that runtime bugs slip past TypeScript on a weekly basis
Don't pick it if…
- →The app is genuinely better-rewritten-than-rescued — we will tell you in the 48-hour audit and quote a rebuild instead
- →You need a framework migration (Next.js pages → app router, CRA → Vite) — scope App Migration separately
- →You need a security audit only — use the $499 Security Audit first
- →You are still prompting AI faster than we can refactor — freeze AI commits during the engagement first
TypeScript refactor engineers who run this cleanup
Cleanup is a refactor pass, a debugging pass, and a written audit. Three specialists cover the surface.
any soup Cursor and Claude Code ship into code a human team can read.Anatomy of a Cursor code cleanup engagement
Every AI-generated codebase we clean up has roughly the same shape when we first open it. The commit history shows rapid bursts of activity — thirty commits in a day, three days of silence, another burst. The file names are plausible but inconsistent: userUtils.ts and user-helpers.ts and users/helpers.ts all living next to each other, each exporting almost the same three functions with slightly different type signatures. The tests, if any, are pinned to implementation details: mocks of the exact module under test, assertions on the number of times a mocked function was called, and zero actual network work. This is the fingerprint of AI-generated code, and it is what the cleanup engagement is designed to undo without rewriting the app.
Week zero is the 48-hour audit. Two senior engineers read the repo with no commits for the first day. We map the duplicated helpers, score the type looseness per directory, list the security gaps (missing auth on routes, leaked secrets, permissive CORS), and draft a prioritized blocker list with an effort estimate per block. The audit is $1,500 fixed, rolled into the cleanup engagement if you proceed. Founders who choose not to proceed still keep the written audit as a baseline for whatever team takes the code next.
Week one is security and data correctness. The rule is: nothing that affects data integrity or user safety waits for week two. If an API route is shipping with no auth check, we patch it first. If a Supabase table is returning rows it should not, we ship the RLS migration before we touch a single helper. The industry benchmark (see our 2026 vibe-coding research) understates how common the very basic misses are in AI-built MVPs specifically; the cleanup pass closes them before any cosmetic refactor happens.
Weeks two and three are the refactor proper. We consolidate the three copies of userUtils into one. We tighten the types at every module boundary using Zod so that the data the UI renders matches the data the API says it returns. We extract the god components into a hook + a view pair so the data-fetching and rendering can be tested independently. Each of these lands as its own commit, tagged so you can bisect or revert without unwinding the entire engagement. The discipline that makes this work is the commit-per-file cadence — no branch sits open for days, no surprise refactor lands at the end of a week.
Week three or four is the guardrail pass. We write the project rules file (.cursorrules, CLAUDE.md, or .windsurfrules) codifying the conventions we just applied. We enable strict TypeScript settings, add ESLint rules that catch the duplication pattern, and wire a GitHub Actions pipeline that runs type-check, lint, and the new integration tests on every PR. The next prompt your founder runs inside Cursor or Claude Code sees the rules file first and stays on-architecture.
The handoff is an architecture doc plus 30 days of AI-PR review. The architecture doc is 4–6 pages: folder conventions, data-flow diagram, error-handling pattern, test strategy, deploy checklist. The AI-PR review is hands-on: for 30 days after handoff we review every pull request that lands on your main branch, flag regressions, and coach the prompting team on how to avoid re-introducing the patterns we just removed. Teams that take the coaching seriously see regression loops drop 60 to 80% inside the first month.
What the cleanup will not do. We will not migrate frameworks, redesign the database, or rewrite the product from scratch. We will not enumerate every security risk — use the $499 Security Audit for that. We will not productionize auth, payments, deploys, and monitoring at the same time — use Finish My MVP if the whole app needs to ship. Cleanup is one discipline: take AI-shipped code and make it readable, testable, and extendable by a human team.
Related refactor services
Related code cleanup case studies
Codebase too messy to touch?
Start with the 48-hour audit — $1,500, rolled into the 2–4 weeks cleanup. We will tell you what is worth keeping and what the refactor actually costs.
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