Building smarter applications: How to leverage AI and Machine Learning

Creating apps that are merely functional is no longer enough. Users expect more: personalized experiences, seamless automation, and rapid interactions. As tech experts, we’re constantly challenged to deliver more intelligence, way faster. That’s where AI and ML step in.

 

The strategic impact of AI in software development

Artificial Intelligence (AI) and Machine Learning (ML) are redefining the way we design, build, and maintain and scale applications. By driving automation and enabling intelligent features like recommendation engines, AI has become a key enabler for modern engineering teams.

Now the question is "How can we make AI work for us?" or “How do we extract productivity out of AI?”. So, let’s explore the path from traditional development to intelligent, AI-enhanced applications, and how to do it with speed, quality, and purpose.

Step 1: Identify the right use cases

AI should never be applied just for the sake of it. The first step is always to define the right business problems where AI can add measurable value. Some of the most impactful areas include:

• Recommendation Systems: Suggesting relevant content or products based on user behavior.
• Natural Language Processing (NLP): Powering chatbots, sentiment analysis, and voice-to-text features.
• Predictive Analytics: Forecasting user churn, sales, or performance trends.
• Intelligent Automation: Streamlining workflows, approvals, and data extraction tasks.

Ask yourself: “What decisions or actions in our app could be improved with predictions or automation?”

Step 2: Build on a solid data foundation

AI is only as smart as the data that feeds it. That’s why building smart applications starts with ensuring your systems can:

• Collect relevant and clean data
• Store and access data securely and efficiently
• Label and process data in ways that models can learn from

Your data strategy must align with your product goals. A close collaboration between engineering and data teams is essential.

And last but not least, although it was somehow neglected in the very early stages of the AI hype we have observed, putting the data quality dimension at the center of the equation has become the critical starting point when it comes to developing an AI solution.

Step 3: Navigating the AI landscape - choosing the right tools

Today’s AI landscape is vast and evolving rapidly. From so very many, powerful open-source libraries like TensorFlow and PyTorch to no-code ML platforms, there’s a wide range of alternative tools which are more accessible than ever. Depending on the team’s experience and project goals, you can choose the right level of complexity and control:

• Getting started? Pre-trained APIs from AWS, Azure, or Google Cloud (such as Vision, Translate, or Speech) allow you to integrate AI quickly without building models from scratch.
• Need deeper integration? Python-based frameworks like TensorFlow or PyTorch — or managed platforms like Vertex AI or Azure ML – offer flexibility to train and deploy custom models.
• Prototyping fast? AutoML tools help teams build and test AI solutions rapidly, perfect for validating ideas before committing to full development.

The key is to choose tools that align with your team’s expertise and the complexity of your product – ensuring a balance between speed, control, and scalability

Step 4: Train, test, and iterate

Machine Learning is an iterative process. It requires training your model on historical data, validating it against real-world scenarios, and continuously refining it – reason why we recommend establishing CI/CD pipelines not just for code, but for ML workflows as well.

Tools like MLflow, DVC, or Kubeflow can help you manage versions, track metrics, and deploy models in production. And always remember, while never getting surprised: AI systems improve over time if given the right feedback loops.

Step 5: Ensure explainability and ethics

As tech experts, we have a responsibility to fully ensure that our applications are intelligent, but also yet fair and accountable. That means:

• Understanding how your models make decisions
• Avoiding bias in training data
• Provide users with transparency and opt-out options

Integrating explainability from the start is mandatory, and resorting to tools like SHAP or LIME can help reveal why your model made a certain prediction.

Step 6: Measure success and optimize

Don’t wait until launch to start thinking about metrics. Define success criteria early on, and you can kick it off by applying ‘traditional’ KPIs for AI features, which contemplate:

• Accuracy, precision, and recall for predictions
• Engagement metrics for recommendations
• Time saved through automation
• User satisfaction for AI-enhanced UX

Finally, once live, monitor performance and gather user feedback to persistently optimize your models and experience.

 

Real-world results: Smarter search with Azure AI

To illustrate what these steps look like in action, let’s look at a recent collaboration with a company in the e-commerce space. Our client, a Netherlands-based software company building integrated ERP and webshop solutions for over 250 retailers and wholesalers, partnered with us to modernize, scale, and continuously evolve their platform.

Among the many improvements we’ve delivered together, one highlight was rethinking the webshop’s search experience using Azure AI Search. By enhancing the underlying search engine with AI capabilities, we made results faster and more relevant for end users, while also giving platform administrators more control over how results are ranked and displayed.

This change not only improved the usability and efficiency of the search experience but also contributed to increased discoverability and higher conversion rates, proving how smart, focused AI adoption can ensure tangible business value.

It’s one of many examples of how we help engineering teams embed AI into complex, business-critical systems – without disrupting what already works.

 

Your partner in AI-powered product development

At 99x Portugal, we combine deep expertise in software engineering with hands-on experience in designing and deploying Data- and AI-driven applications, helping companies turn complexity into clarity and evolve with confidence. Our focus is always on solving real business problems, moving fast without compromising on quality, and delivering long-term outcomes with scalable, explainable AI.

We bring together product engineers, data scientists, and cloud experts to co-create smarter solutions from proof of concept to production. Our global group knowledge and local talent in Portugal give us the perfect mix of depth and agility.

And because every organization’s needs are different, we offer flexible collaboration models to fit your goals, budget, and roadmap:

• Dedicated Team - Long-term, ongoing cooperation where a dedicated, scalable team works as an extension of your own crew. Fixed monthly fee.
• Time & Materials - Perfect for projects with evolving requirements, where effective estimation isn’t always possible. Monthly cost is based on actual time and resources used.
• Fixed Scope Projects - Ideal for initiatives with clearly defined specifications and timelines. Cost is agreed upon upfront based on a fixed price.

 

Let’s talk about your specific challenges and how AI can address them.