You used Cursor, Bolt, Lovable, or Claude Code to ship a SaaS product in days instead of months. The features work locally. The UI looks polished. But AI coding tools optimize for "it compiles and renders" — they do not verify that your Stripe webhook actually delivers events, that your Supabase RLS actually blocks unauthorized access, or that your production environment variables are correctly configured.
What AI coding tools cannot verify
AI generates code that looks correct. But production readiness is not about code correctness — it is about system behavior. Does the webhook endpoint receive events from Stripe in production? Does the database allow queries only from authenticated users? Are your secret keys actually secret? Are your email deliverability records configured? These require live probing, not static analysis.
The 5 most common vibe-coded launch failures
1. Stripe webhook using test-mode signing secret in production. 2. Supabase service-role key exposed in NEXT_PUBLIC_ variable. 3. Auth redirect URIs still pointing at localhost or preview URLs. 4. No rate limiting on API routes. 5. No monitoring — the founder discovers outages from customer emails, not alerts.
Security is not optional after vibe-coding
AI tools do not enforce security best practices. They generate working code, but "working" does not mean "secure." RLS policies may be too permissive, API routes may lack input validation, and CORS might be wide open. These are not hypothetical risks — they are the exact failures PreFlight catches on real vibe-coded projects every day.
Bridge the gap with automated verification
PreFlight is designed for exactly this workflow: ship fast with AI, verify with automated probes. Connect your providers (Stripe, Supabase, Vercel), run a check, and see exactly what is production-ready and what needs fixing. The AI built it — PreFlight proves it works. Ship when the checks are green, not when you hope everything is fine.
Expanded field note
From generated prototype to production proof: the practical answer
fast generation accelerates code, but it also hides assumptions about auth, data, secrets, and failure recovery. This guide is for developers moving an AI-built app toward launch. Start by naming the failure you want to prevent and the customer or operator who would notice it first.
The useful scope is which behaviors need proof before the app carries real users or money. That keeps the work concrete: you can choose a URL, provider, route, release, or data operation, observe it, and decide what to do when the observation does not match the expected contract.
What good looks like
a public audit, provider-aware checks, browser journey, and release baseline. A result is stronger when it preserves the input, environment, timestamp, expected behavior, observed behavior, and the next action. This lets a different person reproduce the finding without asking the original operator to reconstruct the entire context from memory.
the goal is not to distrust AI; it is to verify the deployed behavior it produced. Keep the scope visible when sharing the result. A passing outside-in check can prove a reachable behavior at a point in time; it cannot silently become a guarantee about private code, every authenticated role, or every provider failure mode.
Frame the decision before you change anything
developers moving an AI-built app toward launch usually do not need another dashboard full of disconnected warnings. They need a defensible answer to a narrower question: is the behavior that matters to the customer working in the environment that is about to change? Start there. If the answer is unclear, make the ambiguity part of the work instead of translating it into a green score.
The first useful boundary is which behaviors need proof before the app carries real users or money. Write it down in the same language the team will use during the fix. Name the route, provider, release, role, data object, or browser action involved. Then write the expected behavior as a sentence that could be checked by another person. This turns a broad topic into a small contract and makes it easier to tell whether a failure is reproducible, transient, out of scope, or genuinely fixed.
A good scope also includes what the check does not attempt. Public observation is different from authenticated authorization testing. Provider reachability is different from a complete fulfillment path. A page that renders in a browser is not necessarily a page a crawler can index. Stating the limit early protects the reader from overconfidence and tells the operator when to add a deeper review.
Questions the result should answer
- What was tested? Identify the canonical URL, route, provider, account role, release, or customer action rather than describing the scope as “the site.”
- What should have happened? State the expected response, permission, side effect, delivery event, page directive, or recovery signal in plain language.
- What actually happened? Keep the observed status, safe error, response detail, timing context, or missing side effect without pasting credentials or customer data into the record.
- Why does it matter? Connect the observation to a customer, crawler, revenue, security, availability, or release decision so severity is not just a color.
- What happens next? Name the smallest reversible fix, the owner, the rerun, and the condition that will close the issue.
These questions are deliberately boring. Boring evidence is easier to compare, easier to hand off, and easier to defend later. It also gives an answer engine or a future teammate enough context to summarize the result without inventing a claim that the original check never made.
A sequence that holds up under pressure
- Inventory generated routes and dependencies.
- Search client assets for secrets and unsafe defaults.
- Test data access with multiple roles.
- Exercise signup, payment, and recovery journeys.
- Keep a release baseline and rerun after each meaningful change.
The important nuance is this: the goal is not to distrust AI; it is to verify the deployed behavior it produced. That distinction matters because fast remediation can create a second problem: a broad header change can break a payment script, a credential rotation can break a cron worker, and a restrictive policy can make a valid customer path look like an outage.
Evidence to keep with the fix
| Record | Why it matters |
|---|---|
| Scope | The URL, role, provider, release, and limitation prevent a result from being reused outside the question it actually answered. |
| Before | The original failing observation, environment, and customer impact make the fix auditable. |
| Change | The code, configuration, provider setting, migration, or credential action that should alter the result. |
| After | A rerun against the same scope proves whether the intended behavior recovered. |
| Owner | A named person or team, an expected next action, and a review date keep the result from becoming an orphaned warning. |
| Follow-up | An owner, cadence, or release rule keeps the same class of failure from returning silently. |
Failure modes worth checking twice
- A prototype database remains in a permissive mode.
- Generated environment names put secrets in the browser.
- A happy-path journey is tested without a failure path.
- The team adds more features before fixing the first exposed boundary.
When one of these appears, avoid making several unrelated changes at once. Preserve the failing evidence, isolate the smallest boundary that can explain it, make one reversible correction, and rerun. That rhythm is slower than guessing for the first five minutes and faster than untangling a release that changed three providers at once.
Know when the first layer is not enough
Automation is valuable because it is repeatable, but repeatability is not the same as depth. If the question involves complex authorization, tenant isolation, injection, business logic, a high-value asset, or an adversarial threat model, use the automated result as a map for a deeper review. Give the reviewer the scope, failed observation, relevant release context, and the boundary you want tested. Do not present a public scan as a certification or a substitute for professional security work.
The same rule applies to operations. A successful provider probe may prove that a credential can reach an API, but it may not prove that a webhook creates the correct entitlement. A healthy uptime response may prove reachability, but it may not prove that a signed-in customer can complete the task. Add the assertion or browser journey that matches the real risk, and keep the cheap signal for early warning.
Questions people ask after reading this
What is the fastest useful first step?
Choose one representative scope and write the expected result before running the tool. For this topic, that means which behaviors need proof before the app carries real users or money. A small, explicit baseline is more useful than a large scan whose findings have no owner or decision attached.
What should I do when the result is green?
Keep the scope, timestamp, and limitation, then decide whether the result belongs in a release gate, monitor, report, or follow-up review. Green means the observed contract passed. It does not turn untested behavior into evidence.
What should I do when the result is red?
Read the evidence before changing configuration. Confirm the environment, reproduce the smallest failing behavior, assign the next action, and rerun after the fix. If the issue requires credentials, source access, or adversarial judgment, escalate it rather than hiding the gap behind a retry.
After the fix ships
Run the same check on the canonical production surface, not only on a local or preview environment. If the issue involved a provider, wait for the real callback or scheduled sample. If it involved search, confirm the HTML, canonical, robots, sitemap, and internal links agree. If it involved payments or access, verify the side effect a customer receives rather than stopping at a browser redirect.
PreFlight is designed for this last step: keep the original observation, connect the relevant provider or journey, attach the release context, and let the next run show whether the system stayed healthy. The goal is not a bigger report. It is a shorter path from signal to a verified decision.