01
The situation
Teams shipped AI-assisted prototypes, copilots, and internal tools in a hurry. The repo is a patchwork of generated code, half-merged PRs, and dependencies nobody understands.
Security basics got skipped — exposed keys, loose auth, customer data in prompts or logs — and several “projects” stalled when the person who vibe-coded them moved on.
The failure mode is not “we need more AI” — it is unaudited code in production, nobody who can explain a deploy, and leadership discovering gaps when compliance or a breach asks awkward questions.
02
What you get
We audit what actually exists in code and production, close urgent security gaps, and decide what to finish, fold into your stack, or kill.
You get a prioritised fix list, finished work where the business case is real, and honest “stop here” calls when patching generated mess costs more than rebuilding one slice properly.
When the answer is a thin manual process with a named owner instead of another bot, we document that too — same build-vs-automate instinct as everywhere else.
03
Cleanup work
- Repo and runtime inventory — what is deployed, what is only on someone’s laptop
- Security review — secrets, auth, data handling, dependency and supply-chain risks
- Code quality triage — generated sludge, dead paths, missing tests, undeployable branches
- Finish stranded projects with clear scope — or kill them with plain reasons
- Harden what stays — logging, access control, rollback, human checkpoints where needed
- Handover to someone inside who can maintain it after we step back
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How AI cleanup runs
Audit the mess, close security gaps, then finish or kill — with owners on what stays.
Step 1 Week one
Map what exists
Repos, envs, half-deployed apps, shadow automations — what is live, what is abandoned, who touched it last.
Step 2 Urgent fixes
Security & data first
Secrets, auth, logging, and data boundaries — fix what should never have reached prod before we polish features.
Step 3 No 80% forever
Finish or kill
Stranded projects get an honest scope to ship — or a kill list with reasons, not shame.
Step 4 Handover ready
Harden what stays
Tests, deploy path, ownership, monitoring — maintainable software, not demo-ware.
Another AI tool subscription is not a fix. Knowing what is in prod, who owns it, and whether it is safe to run — that is.
04
Signs this might be you
- An AI-built app or internal tool nobody wants to deploy again
- API keys, tokens, or customer data found where they should not be
- Half-finished project: demo worked, production path unclear
- Generated code with no tests and reviewers who are afraid to merge
- Three overlapping tools doing the same job after different teams experimented
- “It worked in Cursor” but CI, staging, or rollback do not exist
05
Audit before another sprint of vibes
We map repos, environments, and what is actually live — including the fork only one person runs and the automation chain finance did not know about.
Risk, maintainability, and business value beat how impressive the demo looked on a screen share.
06
Secure, finish, or kill — with names on each
Close security holes first. Then finish projects that earn their keep, or kill duplicate and unmaintainable work with plain language.
What stays gets owners, deploy paths, and guardrails — not another layer of generated glue.
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Good fit when
- You inherited or shipped AI-assisted code and need it safe and maintainable
- Projects are 80% done and stuck — you want finish-or-kill honesty
- Someone inside will own the stack after we hand over
×
Probably not when
- You want an AI strategy PDF — start with the free review or process audit
- You are shopping for a chatbot to fix a broken process — map the work first
- Nobody will share repos, access, or where data actually flows