Vibe Coding Examples: 7 Enterprise Use Cases for 2026

Once you look past the demo, vibe coding examples split into two camps. Some are apps now running in production for thousands of users. Others exposed sensitive data because nobody checked the defaults.

You can see both ends in a single pair of numbers. A 156-API HR platform for 25,000 NHS staff sits in the first camp, while a Supabase misconfiguration that exposed 1.5 million API authentication tokens sits in the second.

Below are 7 documented cases from 2026, with what each team shipped and what happened once it was live.

What counts as a vibe coding example in 2026?

Vibe coding means describing an app in plain language and letting AI generate the working code behind it, a term Andrej Karpathy coined in early 2025. Enterprises adopted it well ahead of the policies meant to govern it.

That gap is about to get wider. Gartner projects that by 2028, enterprises will use vibe coding to create 40% of new production software.

That timeline is short, so the examples below, both the ones that scaled cleanly and the ones that leaked data, show what's coming for any team still treating this as a side project.

7 Vibe coding examples: what got shipped and what happened after

1. 🚢 An itinerary tool that replaced a stack of spreadsheets at Virgin Voyages

Virgin Voyages runs four ships, each one juggling restaurants, entertainment, a hotel, and crew logistics at the same time. Planning a single itinerary used to mean pulling numbers across a mess of disconnected sheets, then rebuilding those numbers by hand every time a port changed.

The fix came from inside the business, not from engineering. A Center of Excellence of 10 semi-technical analysts pulled it together after two days of training on the platform.

What they shipped folded that scattered process into one tool. The resulting Itinerary Management Studio, Virgin Voyages reports, now recalculates cost and revenue for a sailing the moment the plan changes.

The same team kept going. Beyond the itinerary tool, they shipped 15+ production apps across 7+ departments in 30 days, with zero dedicated frontend engineers on the build.

2. 🎙️ A crew training simulator running on Gemini's voice models

Before this, practicing a de-escalation conversation with an upset guest meant grabbing a colleague to role-play, if anyone had the time. Virgin Voyages wired Gemini's speech-to-text and text-to-speech directly into a Superblocks app instead.

The Sailor Services Training Simulator now runs live voice conversations with an AI persona, then scores the interaction against brand standards.

The result drew attention well beyond the fleet. The build was significant enough that Virgin Voyages featured it at Google Next alongside Richard Branson and Sundar Pichai.

None of that went through a traditional dev cycle. The whole thing came from a business insights analyst prompting Clark.

3. 🏢 Turning down an $850K vendor quote at Matthews Real Estate

Matthews Real Estate adds roughly 150 brokers a year, and offering memorandums, the documents that anchor every commercial real estate deal, used to take 3 to 5 days of manual formatting per deal.

A senior marketing manager with two decades in real estate but zero coding background built the fix himself. Matthews confirms that his OM Builder now generates a finished memorandum in minutes once deal content gets dropped in.

That in-house app skipped an entire vendor purchase. Superblocks reports that Matthews turned down an $850,000 vendor quote and replaced it in-house.

The OM Builder wasn't the only thing Matthews built. Separately, 800+ agents use an internal AI hub, the GPT Launchpad, for lease abstraction and deal summarization, put together by an AI process engineer six months out of college.

4. 🏥 A 156-API HR platform put together by a designer with no engineering background

NHS Neuron had 25,000 staff using nearly 200 disconnected tools just to deliver HR services at a single trust. Off-the-shelf HR platforms weren't designed to handle that level of fragmentation.

Lukas Saridis studied design, not computer science, and had never shipped a production database connection before this project. That background makes what he built harder to explain away as an outlier.

NHS Neuron reports that his platform, Axon, now runs on 156+ APIs across 45+ pages.

Access even manages itself at that scale, with permissions tied to job posts instead of individual people, so it updates automatically when staff change roles. That is what puts one app on track to replace the ~200 tools the trust used to stitch together by hand.

5. 💊 A 20-person calling team replaced by one prompted queue

A healthcare TPM inherited a process built around a single phone line, where each request waited for one person to take the next call.

The TPM was running a technical build for the first time, and got there through prompts. The queue that came out of it holds up under real clinical load.

Superblocks documents that it now processes 1 million prescriptions a year on a 48-hour SLA, running with SSO, audit logging, and Cloud Prem deployment already in place.

With the app running that volume, the original 20-person calling team was reassigned to other work.

2 Vibe coding examples that show what happens without governance

6. 🔓 The Lovable RLS flaw that exposed 170 live apps

Lovable apps ship with a Supabase backend by default, and Supabase is safe to expose on the client side only when row-level security is on. In hundreds of Lovable projects, it wasn't.

Security researcher Matt Palmer found the flaw in a single Lovable-made site in March 2025, then ran a wider scan once Lovable's fix window closed. The scan showed the problem was systemic, not a one-off.

His disclosure documents that 303 endpoints across 170 projects lacked RLS, letting anyone query usernames, payment status, and API keys without logging in. None of those builders wrote the flaw; they inherited it.

Lovable's own fix, a scanner that checked whether an RLS policy existed, didn't check whether it worked. That missing default became CVE-2025-48757. It was present before any builder touched the project, so every app that inherited the template inherited the same hole.

7. 🤖 Moltbook's "agent internet" ran on an exposed database

Moltbook launched as a social network made for AI agents to post and chat with each other, and its founder said in public that he never wrote a line of code.

During a routine, non-intrusive review, security firm Wiz found a Supabase API key in plain sight inside the site's client-side JavaScript, pointing at a database with no row-level security behind it.

What sat behind that key was the whole user base. Wiz's report confirms the exposure reached 1.5 million API tokens, all readable without logging in.

Moltbook patched the hole within hours of disclosure, and the pattern matches every case above. The AI produced working code, while the one setting that governed whether it was safe to ship went unchecked.

What these 7 vibe coding examples have in common

Line the 7 up and the same split repeats. Every one used AI to generate code that ran, and each one either scaled or leaked based on what the team did with that code after generation.

Virgin Voyages, Matthews, NHS Neuron, and the prescription queue all ran on a platform where IT set the access rules once, before the first prompt went out. Lovable and Moltbook went straight from generation to production, with row-level security as an afterthought instead of a default.

That failure pattern isn't rare. Veracode's spring 2026 testing found that AI models still introduce a known security flaw in 45% of generated code samples when no security guidance is given, even from the newest flagship releases.

The risk shows up in company data too, not just in test benchmarks. One more case makes the point: Flex had a business user build an app holding company IP on a personal Replit account before the company moved it into governance.

In all 7, the generated code ran as written. The security defaults underneath it decided whether shipping it was safe.

How to make your next vibe-coded app production-ready

The 5 examples that scaled cleanly share a pattern you can set up before your next prompt.

Put access control behind the API, not the UI. A hidden button in the frontend still sends the same request, so the API is where the refusal has to happen.

The data backs up why this layer matters. OWASP's latest data found some form of broken access control in 100% of tested applications, which is why NHS Neuron ties permissions to job posts at the platform level instead of leaving each screen to enforce its own rules.

Turn on row-level security before the first user reaches the app. A platform that enables RLS by default covers the exact step both Lovable and Moltbook skipped before their scans caught it.

Route the app through one identity system instead of a fresh login. NHS Neuron routes access through Microsoft Entra ID instead of a database of passwords the app itself has to manage.

"Who built this" should have an answer before anyone has to ask around. The prescription queue runs with audit logging switched on from day one, so that answer exists before an auditor needs it.

Give the domain expert a guardrail before you hand them the build. The marketing manager, the designer, and the TPM behind these builds all worked inside a platform that applied the security rules for them, with nothing left to switch on by hand.

How Superblocks supports governed vibe coding

Superblocks is a governed enterprise vibe coding platform. Business teams build apps with AI, and IT configures the guardrails once instead of finding out what shipped after the fact.

That setup is what the five success stories had in place and what both cautionary cases lacked. Once an app is shipped on Superblocks, the builder no longer has to remember several of the failure points that caught Lovable and Moltbook:

• 🔐 RBAC, SSO, and audit logs in one place: access control sits at the platform level, not inside whatever an individual prompt happened to generate.
• 📊 Every build, query, and integration access gets logged: the Superblocks MCP lets IT query who built what, what data it touched, and when it last ran.
• 🛡️ Secrets never touch generated code: credentials live in the integration layer with role-scoped access, so a prompt never needs a live API key the way Moltbook's frontend did.
• 🔄 A governed home for what's already running elsewhere: builders upload zips of apps made in Replit, Lovable, Claude, or ChatGPT, and Clark migrates them into governance instead of leaving them exposed on someone's personal account.

To see these guardrails against your own stack, book a demo with the team.

Frequently asked questions

What is an example of vibe coding?

A working example of vibe coding is Matthews Real Estate's OM Builder, where a marketing manager with no coding background prompted an AI agent to build an app that generates offering memorandums in minutes instead of days.

Is vibe coding safe for production apps?

Yes, but only when a governance layer sits underneath the AI, enforcing access control and row-level security by default. Without one, generated code carries the same risk that exposed 170 Lovable apps and Moltbook's user database.

What's the biggest risk these vibe coding examples show?

The biggest risk these vibe coding examples show is a missing default, usually row-level security or access control, rather than a mistake in any single prompt. Both Lovable and Moltbook trace back to exactly that.

Can non-engineers really build production apps with vibe coding?

Yes, non-engineers have shipped production apps with vibe coding, including a 156-API HR platform put together by a designer and a prescription-processing queue run by a TPM who'd never written code before this project.

What made those builds safe was the setup underneath them. Both ran on a platform where security was already switched on before they wrote their first prompt.

What's the best platform for governed vibe coding examples like these?

Superblocks is built for governed vibe coding, with RBAC, SSO, and audit logs on by default instead of depending on a builder to configure them correctly.