How We Use AI MVP Development to Get More Leads and Save up to 300 Hours

The modern world of technology is extremely fast, and companies are under a lot of pressure to release products as soon as possible, effectively test and prove the idea, and outrun the competition. The creation of a Minimum Viable Product (MVP) is one of the initial steps towards making a brilliant idea a reality and it is usually a critical one. Nevertheless, the historic method of developing an MVP may be lengthy, expensive, and subject to setbacks.

This is the place where Artificial Intelligence (AI) comes in. Incorporating AI into the process of MVP creation, businesses may save a lot of time when performing such activities as the idea verification, technical audit, code-generation, and testing. Not only does this accelerate the process but it also increases accuracy, reduces costs and assists in getting a product that is ready to hit the market earlier. In this guide, we will discuss how we have applied AI in the development of our MVP to save us up to 300 hours and acquire more leads.

Challenges in Traditional MVP Development

The classic way of building an MVP may seem a very long and zigzagging path with many obstacles on the way. Some of the most important сhallenges that teams encounter are the following:

• Time-consuming estimations and validations: Getting accurate estimates for MVP development can be challenging. Validation of ideas takes time, often delaying decisions and prolonging the presale phase.
• Resource-intensive process: Developing an MVP traditionally requires substantial resources — from manual coding to constant revisions and testing.
• Slow decision-making: The lengthy development process results in delays, and businesses risk missing out on market opportunities.

These are not mere inconveniences; they may impact on the capacity of a business to address the needs of the market and be competitive.

Case Study: How We Used AI for the Poker App MVP

So, what does successful MVP development with the assistance of AI look like in practice? One client came to us with a concept of a poker application where people could play smart and strategic games against AI bots. They required a sanity check and a strong MVP to get fast validation.

The idea was to create a poker application with AI-powered bots, which would be played using artificial intelligence and would be able to play strategic games of Texas Hold-em. The project needed a fast feasibility experiment and an MVP to show to stakeholders. Nonetheless, the development process had some challenges:

• Our team had to identify a Large Language Model (LLM) locally, which would be able to make strategic choices in the game, without using the internet connection. This was essential in making sure that this poker game can be played offline, and the gameplay is not affected much even when there is no consistent access to the web.
• The bots were to be very sophisticated. They needed to respond realistically based on the various game situations, the playing style, and the chip stacks. The bots had to fold, call, raise or go all-in making intelligent decisions depending on the dynamics of the game.
• It was necessary to develop the application that would be compatible with iOS and Android operating systems and provide users with the same seamless experience when using the application on either of the operating systems.
• Another problem was to design an intelligent and scalable user interface. It was required that the design be effective in the MVP and at the same time leave some space to expand as the product would enter further stages of development.

How AI implementation Helped Us

AI played a central role in speeding up the entire development process. Here's how we used AI to solve these challenges:

1. Prompt Engineering

So, how do we get started? Well, we start by designing comprehensive prompts that will make AI produce sensible output. For example, when developing an MVP for a poker app, we created AI prompts that defined the game rules, AI bot personalities, strategic constraints, expected JSON responses, and other critical details like the game's actions (e.g., Fold, Call, Raise). The AI subsequently compiles these directions and comes up with the base code, which forms a framework that conforms to our specifications.

The integration of JSON in our prompts is especially crucial because it ensures that all data passed between components (such as bot behavior, game state, and user interactions) is structured in a way that is easy to process and integrate into the final application. This meticulous configuration is useful in coming up with lifelike game reasoning that reacts to various situations, so that the bot acts within the predetermined parameters.

2. Code Generation

With the prompts ready, we apply AI-assisted tools to develop MVP code. Large Language Models (LLMs), such as Anthropic’s Claude, are particularly effective in generating entire codebases. For example, for the poker app project, AI helped generate the screen structure, poker logic, player behavior, user interface (UI), animations, and the integration of structured data formats like JSON to communicate the game's state and actions. This saves immensely on the time that would be used to write code, as would be the case with developers, and enables the team to concentrate on more important matters and polishing of the product.

It is an automated process that makes the MVP not just work but be well-organized regarding the data flow, which would allow handling the complex aspects of the game, such as player interactions and decision-making processes, with ease.

3. Hybrid Debugging

AI is also important in debugging. As human developers read through the code, AI-enhanced tools find possible bugs, enhance code logic, and accelerate the debugging process. Implementing AI into this step will not only allow us to save time but will also guarantee more comprehensive analysis of the MVP.

As an example, in the poker app development, AI was able to identify problems with the turn-based flow, dealing with cards, the logic of generating prompts, and even with properly handling JSON data throughout the game. This enabled our team to fix bugs fast and therefore we had a more refined product in a shorter period of time.

4. AI Testing

MVP should be tested properly before it is launched. AI-testing tools can automate this process, allowing developers to find and fix bugs fast. An example of these features is a feature we call a Test LLM, which will open a basic chat window that a user can use to test the integration prior to jumping into a game session. Also, this test chat communicates with the backend in structured JSON data, which means that game actions and states are correctly cross-platform synchronized.

Automated testing will allow us to make sure that the MVP is functional on both iOS and Android with the reduced chance of bugs making it through the cracks and guaranteeing a quicker and more dependable delivery.

The Result

With the help of AI during the development process, we managed to reduce the time that would have been spent to develop the MVP of the poker app by more than 300 hours. Not only was the MVP delivered on time but it was also highly functional and could accept feedback which provided our client with a head start in the market.

Benefits of AI in Minimum Viable Product Development

There are several key benefits of integrating AI into MVP development:

How AI MVP Development Helps Get More Leads

The quicker you can get your MVP to market the quicker you can begin to generate leads. AI accelerates the process in the following way:

1. AI enables companies to experiment and prove concepts and functions in comparison to the conventional means, significantly quicker. Automating the testing processes by AI means that only the best ideas will be chosen to be worked on further, saving time and resources. This fast validation implies that you can work on features with the greatest possibility of success.
2. AI tools can reveal user insights, such as user behavior, preferences, pain points, by studying user data. Based on this information, it will be possible to improve the MVP so that it could satisfy the target audience better. This optimization makes the product more in line with the expectations of customers, which increases user activity and conversion.
3. AI also helps businesses to change fast depending on the market response. The continuous analysis of data in real-time can make the MVP evolve continuously so that it remains relevant and satisfies the needs of customers. Rapid iteration ability depending on user feedback enhances the possibility of customer attraction and retention in the long run.

Conclusion

MVP development is changing in terms of how businesses are approaching it with the help of AI. Companies can save a considerable amount of time and resources needed to realize their ideas by incorporating AI tools such as prompt engineering, code generation, debugging, and testing. The outcome is accelerated, more economical MVPs that are more attuned with user requirements and market necessities.

As the AI technology evolves further, additional opportunities will be unveiled to accelerate the product development process, improve its quality, and generate more leads. In order to be on the cutting edge of innovation, AI usage in MVP creation is not an option, but a necessity to businesses anymore.

Are you looking to cut your development time, save hundreds of hours, and release your product faster? AI MVP development may be the boost you are looking for.