Vibe-Coded Prototype to Production: How Software Buyers Changed in 2026

Every few years, the way new software products get started changes. In 2026 it changed faster than usual. Founders and product teams can now build a working prototype on their own with AI coding tools, and that has shifted what they expect from a development partner when it’s time to take an MVP to production.

By Andriy Tsebak, Co-Founder & BDM at NERDZ LAB

We see this from two sides. Our own projects at NERDZ LAB, together with the work of partner agencies we collaborate with, give us a steady flow of real cases to compare. Add regular conversations with founders, CTOs and partners in the EU, the UK and the US, and some patterns become hard to miss.

The main one: AI hasn’t removed the need for an experienced development team, but it has changed what people hire one for. Below are six shifts in client requests we see most often, with the typical request behind each one and the steps we take to deliver it.

In this article:

• TL;DR: 6 shifts in how new software projects start in 2026
• 1. From “build me an app” to “get my MVP to production”
• 2. “AI makes it fast”: where expertise still matters
• 3. AI in the process and AI in the product
• 4. “Will our data train someone’s model?” is now question one
• 5. Test tasks are turning into real work
• 6. Two buyers dominate: serial founders and SaaS-replacing businesses
• Checklist: what to prepare before you talk to a development partner
• FAQ

TL;DR: 6 shifts in how new software projects start in 2026

• Prototype first, development team second. Founders and their teams vibe-code an MVP, then bring in developers to get it production ready and keep growing it.
• High expectations of AI speed. A common belief is that AI makes development very fast and needs little expert involvement. Experience still decides how well a product holds up.
• AI in the process and AI in the product. AI helps the team on every project. Inside the product, it needs a clear use case.
• Security comes first. “Will our code and data be used to train models?” now gets asked before “What’s your tech stack?”
• Test tasks are turning into real work. A good test task solves an actual challenge from the client’s project and can carry straight into it.
• Two buyer types. Serial entrepreneurs testing several ideas, and established businesses replacing SaaS subscriptions with software built around their own processes.

1. From “build me an app” to “get my MVP to production”

The most common first call now goes like this. A founder, or someone on the client’s team, has already built a version of the product with an AI tool such as Cursor, Lovable or Bolt. The demo works, and early users like it. The question is whether it can carry paying customers and their data without falling over.

Six months ago, a typical inbound request was a one-line idea: a travel app, an organizer, a marketplace. Today it’s more often a link to a working prototype and a list of what breaks. We also get more requests to rebuild existing platforms or finish projects another vendor left half done.

A prototype proves the idea. To take an MVP to production, a business needs more from it:

• It keeps working when 10,000 people use it, not just 10.
• Customer data is safe and meets the rules of your market, such as GDPR.
• A new developer can pick up the code later without starting over.
• It gets approved in the App Store and stays up when a connected service fails.

Typical request: “We have a working prototype built with AI tools, and users like it. Make it ready for real customers.”

How we approach it:

• Review the code and test the prototype on real data before giving a price.
• Decide what to keep, improve or rebuild. Keeping what works is often faster and cheaper than starting over, and it keeps the flows your users already like.
• Make it reliable and secure: clean up the code, add automated testing, set up hosting and monitoring.
• Put new features on hold until the product is ready for paying customers.

If you’re in the same spot, your prototype is the best brief you can give a team. It shows how the product should work far better than a 20-page spec. Expect a good team to review it before quoting and tell you plainly what to keep and what to rebuild. (Still at the idea stage? Our guide on how to build an MVP for a startup covers the earlier steps, and our MVP development for startups page explains how we run those projects.)

The AI POC version of the same story

Many of these prototypes are an AI proof of concept (AI POC): a demo showing that AI can read documents, check inspections or write reports. Turning an AI POC into a product brings a few extra risks for the business.

Cost is often the first surprise. An AI service that was cheap during a demo can turn into a noticeable monthly bill once real customers use it, so part of the work is finding cheaper options without losing quality.

2. “AI makes it fast”: where expertise still matters

There’s a popular belief right now that AI makes development very fast and that a project no longer needs much involvement from experienced engineers. It’s easy to see where it comes from. If a working prototype took a few evenings to build, a full product sounds like a few more.

Part of it is true. Our teams use AI every day for routine coding, testing, documentation and first design drafts, and it does shorten timelines.

What AI speeds up most is writing code. The decisions that protect your investment still come from experienced people: how the system is built, how it connects to your other tools, how it handles security and unusual situations, and how it keeps working after launch. AI helps with each of these, and an expert decides what to keep and what to fix. That judgment is what stops a product from becoming expensive to change later.

Typical request: “Our current app was built fast, and now every new feature breaks something. We need a team that can make it stable and keep it that way.”

How we approach it:

• Put together a senior team where every piece of code is checked by a second developer.
• Agree on clear rules for AI: developers hand routine tasks to AI, then check the result and take responsibility for every line.
• Set up a structure and automated tests that let you add features without breaking existing ones.

When you talk to vendors, ask where AI saves you time and money in their process, and who checks its work. Our article on how we use AI across our development workflow shows where we draw that line.

3. AI in the process and AI in the product

Clients now expect AI to be part of how a team works. Many also ask for AI features in the product itself. These are two different things, and they work best together when each one has a clear job.

AI in the process is part of the service we provide. It helps our team on every project, whether or not the product has a single AI feature. AI in the product starts with analysis: for any integration, we first check whether AI is really needed, and in several recent projects the best answer turned out to be a smaller one.

Typical request: “Add AI to our product.”

How we approach it:

• Define the customer problem and the decision AI will help with.
• Compare the options: a ready-made service, a simple AI setup or a custom model. The simplest reliable option usually wins and costs less.
• Add checks on AI answers, and show sources where users need proof.
• In regulated industries such as healthcare, use AI to support professionals’ decisions rather than make them.

So start with the user’s problem and check whether AI is the most reliable way to solve it. When it is, a team that already uses AI daily in its own work is well placed to build it into your product. Our AI development services page has more examples.

4. “Will our data train someone’s model?” is now question one

The first technical questions on a call used to be about tech stack and team size. Now they sound like this:

• How is AI used in your development, testing and design?
• Which AI tools touch our code and data?
• Can you guarantee our code, documents and customer data won’t be used to train AI models?
• Who owns the IP, including AI-assisted code?

They’re fair questions. AI tools and plans differ a lot in how they treat your data, and an agency’s NDA means little if someone on the team pastes your codebase into a consumer chatbot.

Typical request: “Confirm in writing where our data will be hosted and who controls it.”

How we approach it:

• Host in the region the client prefers, with a major cloud provider.
• Register the cloud account to the client’s company from day one, so the business owns its infrastructure and data.
• Put in writing which AI tools are used, how long data is kept, and how backups and recovery work.

Partner platforms check this too, with regular information security reviews of the teams they work with.

Ask for these answers in writing: which AI tools and plans, what the data retention terms are, where the servers are and who owns the cloud account. And check that your contract gives you the IP to all the code, however it was written.

5. Test tasks are turning into real work

Test tasks still matter, and in some ways they matter more now. Anyone can generate a decent-looking prototype, so a test task shows something else: how a team presents its solution, which options it considers and how it explains the trade-offs.

Typical request: “Before we sign, show us how you would solve one real problem from our project.”

How we approach it:

• Pick a real challenge from the client’s scope instead of a generic exercise.
• Solve it and present the options with their trade-offs.
• Adapt the result into the project once work starts.

Selection overall has become more thorough, with milestone plans and effort estimates per role. Relevant experience weighs a lot: a team that has already delivered several similar projects often wins, and that is a fair reason.

If you’re choosing a team, give them a test task built from a real problem in your product, and ask each vendor for the case closest to yours. You’ll see how they think, and you keep the result either way.

6. Two buyers dominate: serial founders and SaaS-replacing businesses

Most new requests now come from two distinct groups of clients.

The second group represents the more significant change. Custom software has become more affordable to build, and companies are reconsidering the traditional “buy, don’t build” approach. Paying monthly per-seat fees for an admin panel where only a fifth of the features are used is harder to justify when a custom solution, built around the company’s own processes, can turn out cheaper over a few years.

Typical request: “Replace the subscription tool our team uses every day with our own app.”

How we approach it:

• Start with a short discovery phase to map the business logic, such as pricing rules, and decide where it should live: in the new app or in existing systems like the ERP.
• Use ready-made services where they fit, for example image recognition, instead of building everything from scratch.
• Launch the core app, then extend it step by step with automation, analytics and an admin panel.

Established businesses also plan further ahead. They often look for a partnership over several years, with a stable core team, in-house developers joining later, and clear terms for support and code handover from the very first conversation.

Before renewing a SaaS contract, it is worth listing which features are actually used and where the team works around limitations. If the gap is significant and the process is core to the business, owning the software may cost less over several years. For generic processes such as email or accounting, SaaS usually remains the better choice.

Checklist: what to prepare before you talk to a development partner

• ☐ A link to your prototype or Figma, plus a list of what breaks or is missing
• ☐ Your target users and how many you expect in the first year
• ☐ Integrations you need: payments, CRM, ERP, existing databases
• ☐ Compliance requirements for your market and industry
• ☐ The SaaS tools you want to replace, and what you pay for them
• ☐ Where AI must be inside the product, and where it’s just nice to have
• ☐ Your questions on AI tools, data use and IP ownership
• ☐ A budget range for the build and for 12 months of support

FAQ

Can a vibe-coded MVP be taken to production?

Often, yes, but rarely as-is. A development team usually keeps the flows and UI ideas, then improves or rebuilds the technical foundation: data, security and integrations. An audit of the prototype is the fastest way to know which parts can stay.

What’s the difference between an AI POC and a production AI product?

An AI POC proves that a model can do the task on sample data. A production AI product does it reliably on real data, at a known cost per use, with QA on its outputs, clear data handling and a fallback when the model fails.

Does AI make software development cheaper in 2026?

It makes some parts faster, mainly routine features, testing and documentation. Architecture, integrations, QA and long-term support still take real engineering time, so expect savings, but smaller ones than the hype suggests. Our guide to app development costs in 2026 covers the build side in more detail.

How do I make sure my code and data aren’t used to train AI models?

Ask your vendor which AI tools they use, on which plans, and what the data-retention terms are. Put data handling and IP ownership in the contract, not only in the NDA.

Should I replace my SaaS subscription with custom software?

Consider it when the tool is core to your operations, you pay for many unused features, and you work around its limits every day. Keep SaaS for generic functions where customization adds little value.

How much does post-launch support cost?

It depends on the product’s size and the response times you need. Many teams offer a fixed monthly package with a set number of hours and an SLA for critical issues. Ask for it together with the build estimate.

Have a prototype that needs to grow up?

Send us the link to your prototype or AI POC. We review and audit it, then tell you plainly what can stay, what needs rebuilding, and what it will take to launch and support it. Book a call with NERDZ LAB