Blog Artificial Intelligence 🤖 Building AI-powered products: From concept to deployment

Building AI-powered products: From concept to deployment

Q: What are the four stages of AI product design?

The four stages of AI product design are: Problem framing: What exactly are you solving, and why does it need AI? Data and feasibility: Do you have the data needed to train or fine-tune a model? Prototype and test: Build a prototype of the product and see how it performs in real-world conditions. Iterate and deploy: Improve the product based on feedback. Monitor and maintain the model and scale it if needed.

Artificial Intelligence 🤖

Oleksandr Budnik

Oleksandr Budnik

Chief Technology Officer

Oleksandr is innovative, collaborative, and results-driven. Knowing that his work can transform businesses, improve lives, and push the boundaries of technology fuels his dedication to working hard and striving for excellence.

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Artificial Intelligence

May 22, 2025
Reading Time: 5 minutes

Reviewed by Altamira team
Edited by Ruslan Grigurko

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Table of Contents

Artificial intelligence has become a part of everyday life. Customers expect AI to drive recommendations, deliver personalisation, and provide instant answers.

Businesses are also interested in using AI in their products, seeing it as the main driver of success.

However, insisting on AI integration without a clear use case often results in a product no one needs.

To avoid this, you need to understand why AI-powered products succeed in the first place.

Why AI-powered products are on the rise

According to Statista, by the end of 2024 the market for artificial intelligence was $184 billion. By 2030, it is projected to exceed $826 billion. This aligns with a McKinsey report showing that 72% of businesses have adopted AI in at least one function.

There are two key factors behind this rapid growth:

Data availability increased

Computational power has improved significantly

AI in 2025 has become far more advanced compared to 2020. Its reasoning has improved, the multi-step logic became better, and the models are cheaper to maintain.

Sam Altman, CEO of OpenAI, shared his thoughts on the trajectory of artificial intelligence and its long-term impact at a TED Talk recently.

Businesses recognise AI’s value, resulting in widespread integration of AI-powered solutions. Despite the enthusiasm, 85% of AI initiatives fail. How come the percentage is so high? The main reasons are rushed decisions that lead to solutions without a clear purpose.

Building your AI product: where to start

The most common trap businesses fall into is starting with tech instead of addressing user problems. Here’s where you should start.

Define the problem and objectives

Start by identifying tedious tasks your users face. Ask yourself:

What processes feel slow or frustrating for users?

What decisions do users make repeatedly?

Where is data accumulating without being used effectively?

These are strong indicators of where AI can create a meaningful impact. Once you’ve identified the problem you want to solve, set your expectations straight. Establish measurable goals and define success criteria early to track whether your AI solution is delivering tangible benefits.

Assess feasibility

Almost everyone knows that AI models are as good as the training data. Assess whether your company’s data is clean, complete, and representative of the problem you’re solving.

At the same time, AI development demands a lot of computational resources. Check whether your current infrastructure can support model training and deployment. If not, explore cloud platforms, such as AWS, Azure or Google Cloud Platform, to ensure the scalability of your model.

Choose the right approach

The right model training method heavily depends on your data and the issue your model aims to resolve. Usually, businesses choose one out of these four.

Supervised learning

Uses labelled data, meaning that all of the inputs have the correct corresponding outputs. This method trains a model to make predictions on new, unlabelled data.

Unsupervised learning

Uses unlabelled data to encourage the model to seek patterns. The model operates without any human guidance and uses only its internal logic.

Semi-supervised learning

A mix of both previous methods. The idea is to use a small amount of labelled data as a reference point and a large dataset of unlabelled data.

Reinforcement learning

Uses rewards to encourage the model. Similarly to the trial-and-error method, the model’s actions towards the goal are reinforced, while other actions are ignored.

Building and validating the model

Building AI models requires a lot of expertise and patience. Natalia Semak, Chief Delivery Officer at Altamira, says that:

In traditional engineering, progress is often steady and visible. Every sprint delivers something tangible. But machine learning doesn't follow that pattern. The progress is nonlinear, often hidden, and sometimes counterintuitive

Data scientists and engineers usually use iterative development to work around nonlinear progress. The model goes through tests and refinement each iteration to reach optimal performance.

Prototyping the future product

When building a prototype, focus on a narrow, well-defined use case with real user value. Gather user feedback to understand the prototype’s performance and the validity of your assumptions.

Test the model

When testing AI models, don’t focus solely on accuracy scores. While accuracy is an important metric, it doesn’t fully reflect real-world performance, especially with ambiguous inputs and edge cases. Test the model for hidden biases and begin monitoring its performance early to detect regression and observe how it responds to changes in the data.

Integrating the model into the product

Once the model is fine-tuned, it’s ready for integration. Monitor the model’s performance and how it interacts with users. Maintain the model and check for deviations in the model’s responses.

Scaling an AI-powered product

Nobody wants to use a product that lags or completely breaks under increased user load. A good call would be to keep your model modular so it could be changed and improved without breaking other components.

Keep in mind that with the influx of users, their expectations around reliability, security, and transparency will increase as well. Addressing growing expectations shouldn’t be an afterthought but built in from the beginning.

Final words

In a world where businesses constantly try to reach changing user needs and expectations, AI-powered products help not to fall behind. Their ability to adapt and evolve makes them perfect solution for user’s struggles and a quality service provider at the same time.

However, to build a successful product, don’t focus only on AI. Instead, think about how AI could improve user experience and how AI can make users’ lives easier.

How Altamira can help

If you want to build an AI-powered product, assess your business’s AI readiness, or test out new ideas, our approach is guaranteed to reduce your stress.

Zero headaches: With a proven track record of pushing the boundaries of what’s possible with AI, we will guide you in avoiding common pitfalls and ensuring your success.

Results in days: We focus on delivering tangible value in days, not weeks. Clickable prototypes demonstrate our commitment even before a contract begins, allowing you to visualise and test your AI concept quickly.

Control everything: Transparent communication guarantees that you always stay in the loop and won’t have to worry about the details.

Get in touch and start building future-proof products today.

FAQ

How do I create my own AI products?

To build a successful AI product, you need a lot of niche expertise and experience in the AI field. However, it doesn’t mean you must have a whole team of data scientists and engineers to build it.

Consider partnering with Altamira to build a successful AI product. We help our clients move forward while staying curious and testing new tools and methodologies to deliver the best product possible.

Once you partner up, start small. Pick a problem you understand well and think about how AI might help solve it. Use existing tools and APIs, get feedback, improve the model, and then scale it.

How are AI products built?

Building an AI product looks similar to any other tech product. The only difference is that AI relies on data, not just code. The process typically involves:

Defining a problem worth solving with AI

Gathering and preparing the right data

Choosing or training a model

Integrating the model into a usable product (e.g., a web app, mobile app, or API)

Testing with real users to refine the product

Most teams use pre-trained models and adapt them to their use case, rather than starting from scratch.

What are the four stages of AI product design?

The four stages of AI product design are:

Problem framing: What exactly are you solving, and why does it need AI?

Data and feasibility: Do you have the data needed to train or fine-tune a model?

Prototype and test: Build a prototype of the product and see how it performs in real-world conditions.

Iterate and deploy: Improve the product based on feedback. Monitor and maintain the model and scale it if needed.

Which AI product to develop?

Firstly, understand that building an AI product just for the sake of using AI will lead to wasted resources and time. If the product could’ve been as useful without AI, it most likely didn’t need artificial intelligence in the first place.

Secondly, look for repetitive, time-consuming tasks or those that require pattern recognition. These are the tasks that benefit from AI the most.