White-label developer support: code reviews, cover and AI-built sites done safely

White-label developer support is specialist help that a developer or an agency brings into a client’s project and passes on under its own name. It usually takes one of four shapes: a remote session, a written review of a repository or pull request, a small scoped task, or ongoing cover each month. Done properly, it starts with an NDA where you need one, runs on the least access that does the job, and ends with that access removed.

This guide is for a freelance developer stuck on one part of a build, an agency that needs a specialist without making a hire, and a business with its own developer that wants a second opinion. Some of those projects start in an AI tool such as Lovable, Bolt, v0 or Cursor, so the second half sets out how to take one to production safely. Which AI tool suits which job is a separate question, covered in AI-assisted vs AI-generated websites; this guide is about the checks.

What does specialist developer support look like?

Pick the shape from the problem, not the other way round.

Shape	Good for	What you get back
A remote session	A problem you can show in an hour: a failing deploy, a policy blocking a script, a page Google will not index	The fix made with you on a screen share, and notes on what was changed
A written review	A repository or a pull request you want a second opinion on before it ships	What was found, why it matters and what to change, in order of importance
A small task	One scoped job you would rather hand over	The work on a branch or in a pull request, for your review
Ongoing cover	Questions, reviews and fixes that come up every month	An agreed amount of specialist time each month

A written review is the one most often undersold. A good one does not list every nit a linter would catch. It ranks the findings, says which ones would hurt a customer or a search result, and says what to change, so you can decide what to merge.

The specialist ground matters as much as the shape. Ours is Astro on Vercel, and the standards work that applies to any site: security headers and content security policies, structured data, Core Web Vitals, accessibility and forms that reach a CRM. Developer and agency support sets out what we help with.

How does white-label work, and do you need an NDA?

White-label means the work goes to your client under your name. In practice, a white-label specialist should:

• stay out of sight of your client, and not contact them unless you ask;
• leave the work out of their own portfolio;
• not approach your client to offer their own services, during the work or for an agreed time after it;
• let you pass on their reviews and work under your own name.

The alternative is to name the specialist as yours, which some clients prefer when a review is going to their own board or developer. Either way, write down which it is before the work starts.

An NDA belongs before the first look at the code or the client. Read what it covers: the code, the client’s identity and any data, and how long the duty lasts after the work ends.

White-label does not switch off data protection. If the specialist will see or reach your client’s personal data, they are processing it for you, which makes them a sub-processor under UK GDPR. The ICO says “the processor should not engage another processor (a sub-processor) without the controller’s prior specific or general written authorisation”, and that “it must put a contract in place imposing the same Article 28(3) data protection obligations on that sub-processor”. So check your contract with your client allows it. Often the simplest answer is to design the access so no personal data is in reach at all: a staging copy with test data, or a screen share of the part that matters.

What access should a specialist have, and what happens to it afterwards?

The least that does the job, for as long as the job lasts.

• For a review, read access to the repository is usually enough. GitHub’s roles for an organisation run Read, Triage, Write, Maintain and Admin, and GitHub’s own advice is to choose the role that fits “without giving people more access to the project than they need.”
• Watch the personal-account trap. GitHub says that “in a private repository, repository owners can only grant write access to collaborators. Collaborators can’t have read-only access to repositories owned by a personal account.” If the code sits in a personal account, move it to an organisation first, or share a screen instead.
• For a task, write access to the repository, with the work done on a branch and merged by you. A specialist rarely needs to push to your main branch.
• For hosting or DNS, a member role on the host for the length of the task, not a shared login.
• For secrets, as little as possible. A review should not need your production keys. Where a task does, the key goes in through the host’s own settings or a hidden prompt, never pasted into an email or a chat.

When the work ends, the specialist takes themselves off everything they were given, any credential they saw is rotated, and you get that confirmed in writing. Our own terms for this work, including access, white-label and confidentiality, are in section 3c of our terms.

How do you take an AI-built site to production safely?

Lovable, Bolt, v0 and Cursor can get a site to a working preview quickly. Production asks different questions, and none of them is about which tool you used. This is the order we work through.

1. Get the code into a repository you own

Everything else depends on the code living somewhere with a history. Where the tool can push to GitHub or give you a download, start there. Lovable’s GitHub integration, for example, is a “two-way sync”: “Changes made in Lovable sync to GitHub” and “Changes pushed to the active GitHub branch sync back into Lovable”, and it syncs one branch at a time. That makes a simple discipline worthwhile: do the production work on a separate branch, review it, and merge it when it is ready, so a half-finished change never lands in the tool’s working copy.

2. Find every secret

Anything in the code a browser downloads is public. If the project builds with Vite (check its package.json), Vite’s documentation is explicit: “Variables prefixed with VITE_ will be exposed in client-side source code after Vite bundling”, and they “should not contain sensitive information such as API keys.” Search the code and the built files for keys, move anything private to the server side, and keep local environment files out of git: Vite says “you should add *.local to your .gitignore to avoid them being checked into git.”

If a secret was ever committed, GitHub’s advice is to deal with the secret before the history: “as a first step you need to revoke and/or rotate that secret.” Rewriting history does not reach copies that already exist “in any clones or forks of your repository.”

If the app has user accounts, a database or payments, whether that data is safe is a question for an application security review. That is the specialism of our sibling practice, Dacros, and it is worth having before launch.

3. One package manager, one lockfile, frozen installs

An AI tool can add packages as it goes. Before launch, pick one package manager, commit its lockfile, and make the build install from that lockfile exactly. With pnpm, --frozen-lockfile means pnpm “fails to install if the lockfile is out of sync with the manifest”, and it is on by default in CI. A failed install is the point: the build stops rather than shipping a different set of packages from the ones you tested. Mixing package managers in one project is one way a lockfile drifts out of step in the first place.

Then check what is installed: pnpm audit “checks for known security issues with the installed packages”, and --prod limits it to the packages that ship. Remove anything the tool added that nothing uses.

4. Make sure search engines can read the pages

An AI-built site may draw every page in the browser, with the content arriving only after a script runs. Google processes JavaScript sites in three phases, crawling, rendering and indexing, and its own advice is that “server-side or pre-rendering is still a great idea because it makes your website faster for users and crawlers, and not all bots can run JavaScript.” Check what Google actually sees with Search Console’s URL Inspection tool and its rendered HTML. For an app that switches pages in the browser, Google says to “use the History API to implement routing between different views of your web app” rather than fragments, and an unknown address should send the visitor to a page the server answers with a 404, or carry a noindex, so it does not become a soft 404. Every page needs its own title and description.

5. Structured data that matches the page

Check any markup the tool added against the page itself. Google’s rule is “don’t mark up content that is not visible to readers of the page”, so a rating, a price or an FAQ in the markup must also be on the page, and true. Google also says it “does not guarantee that your structured data will show up in search results, even if your page is marked up correctly.” Test the result with Google’s Rich Results Test.

6. Accessibility, checked by a person

Aim for WCAG 2.2 at level AA. Run an automated checker, but do not stop there: the W3C says “web accessibility evaluation tools can not determine accessibility, they can only assist in doing so”, and “human judgement is required.” So walk every key journey with the keyboard alone, check focus is visible and the forms are labelled, and try the main pages with a screen reader. Our guide to website accessibility for UK businesses covers the law behind it.

7. Test what a customer will do

Send a real enquiry through every form and confirm it arrives where it should. Check that a missing page returns a 404, that every old address redirects if the site replaces another, and that the build passes from a clean install, not only on the machine it was built on. The launch checklist has the owner’s side of the same checks.

8. Confirm the deploy, then check the live site once

A green build on your own machine is not a deploy. Confirm the production deployment itself finished, then load the live address for the specific change. On Vercel, vercel inspect with --wait blocks until the deployment completes, and Vercel’s documentation adds a warning worth remembering: “A timeout does not mean the deployment is ready.” Check production once. Screenshots, Lighthouse runs and repeated checks belong on a local build, so they never get in the way of real visitors.

The practices we follow on our own builds

These are the rules we hold ourselves to on our own sites and the sites we run, whichever AI tool is helping:

• One package manager per project, its lockfile committed, and installs that fail rather than guess when the lockfile is out of step.
• A deploy is not finished until the production deployment reports it is ready and the live address shows the change.
• Production is checked once. Screenshots and performance runs happen against a local build.
• No secret is printed, pasted, logged or committed. Secrets go in through a hidden prompt.
• Nothing that looks unused is removed until we have checked nobody else is part-way through a change to it.
• Rules and code are read from the production branch, because a local copy can be out of date.
• One instruction file in each repository, read by every AI tool that works on it, so the rules hold whichever tool is working.

None of these is clever. Each one exists because skipping it fails quietly: the site looks fine on the machine it was built on, and the problem only shows on the live site, or not at all.

We have written these practices up, step by step, for anyone setting up their own editor with an AI coding agent: the AI coding workspace guide is a paid PDF with a starter rules file and a checklist.

Where does a security review fit?

A security review of an application, including one built with AI tools, is not part of our developer support. Our sibling practice, Dacros, offers it under its own terms. We look after the website side: security headers and policies, the forms, and how the site is built and deployed. If your project needs both, say so and we will tell you which part goes where.

When to bring in specialist help

Bring someone in before the problem becomes an emergency: before a client launch, before a large pull request merges, or when an AI-built site has reached the edge of what the tool can do. If you are a developer or an agency, tell us what you are working on and we will quote a session, a review, a task or cover to the job. If you built a site with AI yourself and it is stuck, help with a site built with AI is the place to start.