From Concept to Creation: How to Develop AI Software

AI has moved out of the science fiction domain; it is now in your inbox, your playlists, and your business applications: no robotics lab or billion-dollar budget required.

In this article, you’ll find an actionable roadmap to AI software development, from the very idea to its successful implementation. We’ll cover the essentials and how to make an AI that brings value to the users

AI is no longer just for the big players. With today’s tools, even small teams can build smart solutions. Let’s dive into how.

The 3 Types of AI

To avoid confusion (and sci-fi-induced panic), AI is generally categorized into three types:

• Artificial Narrow Intelligence (ANI) is currently the only type of AI we have. It is based solely on performing a specific task well — i.e., generating text, recognizing faces, or fraud detection. Everything is currently available in terms of AI applications, from ChatGPT to Tesla’s Autopilot, all of which qualify under this category.
• Artificial General Intelligence (AGI): A theoretical AI that can perform any intellectual task that can be performed by humans, without being retrained. It hasn’t been developed yet.
• Artificial Superintelligence: A theoretical idea in which computers are superior to humans in all things. Also, something fictional — and irrelevant to product creation.

Real-World Applications and Examples of AI

AI applications such as ChatGPT, DALL·E, Siri, and Tesla Autopilot are transforming day-to-day life by addressing actual pain points of users, from content creation to automation of work. In creating AI software, begin by determining which problem your tool will address and how it will enhance everyday life.

In building AI solutions, most of the applications fall under several broad categories:

1. Natural Language Processing (NLP)

Artificial intelligence that can translate and produce human language. Used for:

• Chatbots and Virtual Assistants;
• Auto emails and responses;
• Language Translation and Summarization.

2. Computer Vision

Artificial intelligence that can read and process visual information. Utilizes:

• Facial recognition systems;
• Manufacturing Quality Control;
• Self-Driving Vehicle Guidelines.

3. Predictive Modeling

Machine learning that forecasts outcomes based on past information. Used in:

• Sales and Demand Forecasting;
• Customer Churn Prediction;
• Fraud Detection in Finance.

AI Across Industries

Artificial intelligence is not limited to one industry. It is revolutionizing the operating models of multiple industries, like:

Whether you are developing a chatbot for customer support, a medical image analyzer, or a stockout prediction program, there is space for AI — and it’s closer than you might think.

Read more about AI across industries

Steps to Build AI Software

Define the pain you solve with AI

Before you jump into steps to build AI software – model training, data wrangling, or picking the “latest LLM,” stop for a second. AI doesn’t solve everything. Most AI projects fail because the problem wasn’t clearly defined.

So, to begin with, what is the pain?

Begin with the Problem — Not the Model

AI is not the goal — it’s just a tool to solve real problems. Ask:

• What do people complain about over and over again?
• What’s repetitive, predictable, and annoying?
• Are people ready to buy the solution?

If your customer support is buried under tickets, maybe AI can help them. If your pricing changes manually every week, maybe it’s time for dynamic pricing. If you’re losing users without knowing why — hello, churn prediction.

Set SMART Goals

A successful AI project starts with a definite goal. Not “let’s make our app more intelligent,” but:

• Decrease content creation time by 40% within 2 months
• Automate SEO-optimized product descriptions for 1,000+ Shopify items
• Forecasting customer churn with a minimum of 85% accuracy.

And how do you define success? Key metrics include accuracy, cost reduction, time savings, and user adoption. Track both technical and business success.

Align Business and Tech Early

Here’s a classic trap: product teams want “magic,” and engineering teams deliver… something else entirely.

Avoid this by getting everyone on the same page before development starts. That means:

• Defining the problem and goal together
• Agreeing on what “success” looks like
• Mapping data sources and technical feasibility
• Clarifying limits: what AI can and can’t do

Data Preparation and Collection

Let’s be honest: AI is only what you feed it. To create effective AI, you need quality data — plain and simple. And here’s where you can take it:

Types of Data: Know What You’re Working With

Not all data is created equal. Here’s the basic breakdown:

• Structured data – Neatly organized in rows and columns, like spreadsheets or databases.
• Unstructured data – Text, images, audio, or video.
• Labeled data – Data with assigned categories, such as product reviews marked positive or negative.
• Unlabeled data – Raw content without tags; it needs to be mapped before training supervised AI.

Where Do You Get This Data?

Here are some options that don’t involve shady data scraping or asking your intern to copy-paste for 100 hours:

• Internal databases – Your product, CRM, website, customer support chats, etc. Hidden gem, if handled right.

• Public datasets – Free and ready to explore. Try:

• • Kaggle – a huge variety of datasets for everything from NLP to image recognition.
  • UCI Repository – classic academic datasets.
  • Google Dataset Search – a search engine just for datasets.
• APIs – Pull live data from external sources like social media, financial markets, or weather services.

A reminder: always check licensing terms before using public data for commercial projects. You don’t want your AI sued. 

Yes, You Will Have to Clean It 

Good data can be messy forts. Clean your data before feeding it to your model. 

• Remove Duplicates – There is no need to have your AI learn twice. 
• Deal with missing values – impute, drop, or flag them – but don’t ignore them. 
• Correct inconsistencies – Standardize nomenclature (e.g., “USA” vs “United States”) and have your categories consistent. 

Pre-processing: Mini Makeover before AI training 

Even clean data needs to be formatted somewhat before a model can use it. If you want to build an AI model that performs well, proper preprocessing is a critical first step. Some common methods include: 

• Normalization – Put features on an equal footing so that your model will not make “salary” more significant than “rating” just because it’s in bigger numbers. 
• Tokenization – Text. Divide sentences into words or phrases that can be understood by the model. 
• Encoding – Translate categories such as (e.g., “red,” “green,” “blue”) into numbers that the model can process. 

Watch Out for Ethical Pitfalls 

Your AI can be trained to learn things you don’t intend to  — like bias. 

• Data bias – Your model will become biased if your dataset is. An example is a recruitment model trained on only previous male applicants. It may ignore equally capable females. 
• Privacy – Don’t collect or use personal information without consent. Anonymize if possible. Laws like GDPR and other data protection regulations are mandatory, not optional. 

It is about fairness – AI should provide unbiased, clear analysis without negative patterns. Not only is that good ethics — it’s good business. 

Choosing the Right Tools and Technologies 

Curious how to build AI tools? Selecting the right stack is the first step toward creating something functional and scalable. 

For Developers: The Classics That Power Most AI Projects 

• Libraries 
  • TensorFlow & PyTorch – Best suited for deep learning. 
  • Scikit-learn – Rapid prototyping with basic models. 
  • Hugging Face Transformers – Pre-trained, Ready to Use NLP Models.
     
• Languages: 
  • Python – Industry standard choice. 
  • R – Most appropriate for statistic-heavy work. 
  • JavaScript – Best for lightweight, browser-based models. 

For Non-Technical Founders: No-Code Tools 

No coding? No problem. With these products, you can make a simple AI without typing a single line of code: 

• Builder.ai, Pecan.ai, Akkio – Drag-and-drop AI for predictions, recommendations 
• MLaaS platforms such as AWS SageMaker, Vertex AI from Google, and Azure ML Studio.  

They all offer end-to-end tools from training to deployment. 

Training and Building AI Models 

AI models can be built from scratch or adapted from pre-trained ones, which are already widely used across industries like healthcare, e-commerce, and customer service. 

Pretrained or From Scratch 

Use pre-trained models when:
You require speed in turnaround times, or you are dealing with commodity tasks like sentiment analysis or image classification

Train from scratch when:
Now that your data is clean and ready, it’s time to put it to work by training your model. So, how to build an AI model? Let’s start. 

Basic AI Model Training Workflow: 

• Split your information: 
  • Training dataset – teaches the model 
  • Validation set – refines it 
  • Test set – checks performance on fresh data 
• Fit the model: Provide training data and let the algorithm find patterns
• Validate & test: Troubleshoot accuracy and detect overfitting (when the model memorizes instead of generalizing). 

Different Types of Learning 

Not all AI learns alike. These are three primary modes through which it learns: 

• Supervised learning – You present labeled data to the model (e.g., emails labeled “spam” or “non-spam”) and the model learns to make predictions based on these labeled examples. Great for classification and regression tasks

• Unsupervised learning – No labeling. The model learns to sort or partition by itself. It can be applied to perform clustering, segmentation, or inconsistency detection

• Reinforcement learning – It learns by trial and error, like training a robot to walk or a robot that plays chess. Most suited for decision-making issues in dynamic environments. 

Common Algorithms to Know 

• Decision Trees – Easy to understand with a straightforward approach. Suited for short-term victories. 
• Support Vector Machines (SVMs) – Excellent for classification in high-dimensional space. 
• Convolutional Neural Networks (CNNs) – De facto image processing. 
• Recurrent Neural Networks (RNNs) – Ideal for handling time-series or sequential information (for example, speech or stock prices). 
• Transformers – Empowering the latest language models like BERT and ChatGPT. 

How to Measure Your Model’s Performance 

Your model isn’t useful unless it performs well, and the way you measure that depends on what you’re predicting. 

• For classification tasks (e.g., “Is this email spam?”)
  • Accuracy – % of correct prediction
  • Precision – Of the positive predictions, how many were correct
  • Recall – Did we catch all the positives
  • F1 Score – A balance between precision and recall
• For regression tasks (e.g., “What will sales be next month?”)
  • RMSE (Root Mean Square Error) – Penalizes larger errors
  • MAE (Mean Absolute Error) – Average of all absolute errors 

Use multiple metrics to avoid false confidence. A model can have high accuracy but terrible recall, especially in unbalanced datasets. 

Tuning & Optimization (or “Making It Better”) 

Once you have trained your model, you will probably need to fine-tune it to obtain the best result: 

1. Cross-validation – Splitting data into folds for improved performance testing. 
2. Tuning hyperparameters – Fine-tuning values like learning rate, tree depth, or number of layers for best fit. 

It can be done manually (slow), or by employing tools like Grid Search, Random Search, or Optuna (faster, cleverer). 

Deployment and Integration 

Training a great model is cool. But if it just sits on your laptop, it’s an expensive spreadsheet. To make AI useful, you need to deploy it and plug it into your actual business. 

How to Deploy an AI Model? 

There are several standard methods to deploy your AI model to the physical world: 

• RESTful API – Wrap your model in an API with frameworks like Flask or FastAPI. This makes it easy to feed information to your model, as well as get predictions from any application, service, or dashboard
• Containers – Containerize your environment and model with Docker so that it runs exactly alike everywhere — locally, on servers, or in the cloud
• Cloud Platforms – AWS SageMaker, Vertex AI from Google Cloud, or Azure ML are some of these services that handle hosting, scaling, and managing your model.

Bonus: zero servers to manage yourself. Your team’s infrastructure and skill set should dictate your solution. Cloud solutions are scalable, API calls are light, and containers are portable. 

Plugging AI Into Your Existing Stack 

A model will only be effective if it reflects the way your business is already running. That means integrating it with such tools as: 

• CRMs: use AI to score leads or auto-fill contact data; 
• ERPs: demand forecasting, inventory planning; 
• Support tools: automated ticket filtering; 
• Marketing technologies: AI-copywriting, dynamic content optimization, campaign performance predictions; 
• Tools for sales: deal probability scoring, smart next-best actions, and revenue forecasting; 
• Productivity tools: meeting notes summarization, automated notes, or task generation from emails/chats; 
• Media & content tools: AI to edit image, video, or sound; to create branded assets; or to translate content; 
• Internal operations: automated routine tasks like scheduling, reporting, and approvals; 
• HR & talent solutions: AI-driven candidate screening, intra-company mobility matching, or engagement prediction. 

Working with your dev team or product lead to define when and how predictions get triggered — and who uses them. 

Tools to Scale Like a Pro 

If you have thousands (or indeed millions) of predictions, some serious heavy-hitting tools are going to be required: 

• TensorFlow Serving – Deploy models in production at scale with support for low latency. 
• Kubernetes – Automatically deploy, scale, and manage models that are containerized. Best for workloads with burst or high variation. 

They are not required for small projects, but become increasingly important with expansion. 

Monitoring: Because AI Can Drift (and Break) 

Deployment of the model is just the beginning. AI models don’t stay perfect with time, especially if people’s behavior or data changes over time. Hence, there is a need for monitoring of: 

• Latency – How responsive is your model? 
• Accuracy drift – Are we still getting accurate predictions from each new batch of data? 
• Error rates – Is there anything unusual or erratic about predictions? 

Set reminders and review performance on a consistent basis. It is like checking tire pressure — boring, but essential to avoid a blowout. 

Maintenance and Continuous Improvement 

Your model can degrade over time. It’s usually due to model drift (the relationship between inputs and outputs changes over time) or data drift (the input data changes over time). 

A model based on 2022 information might not identify 2025 trends – because people and markets change. 

Keeping it Fresh: Ongoing Retuning 

Models must be retrained regularly to remain accurate. Automated pipelines can be created with the help of tools like: 

• MLflow – Model management and experiment tracking. 
• Kubeflow – To orchestrate end-to-end workflows for production-scale retraining. 

It enables you to retrain models based on fresh information without repeating work every time. 

Use Feedback Loops

Update with real-world information— e.g., user behavior, corrected forecasts, or new labels — to continue training the model over time. For example, if users repeatedly click on “this recommendation is not accurate,” that is valuable information for training. 

Cost Considerations and Budgeting 

Creating AI software can be inexpensive if you’re aware of where your money is going. What follows is a breakdown of typical costs.

Budgeting Tips 

While building an AI project, begin small and incrementally scale up. Build a Proof of Concept (PoC), a minimal version of your idea built with minimal available resources, barely sufficient to prove that the idea upon which it is based is possible. To save time and money, attempt to use pre-trained models, especially for generic tasks like text generation, classification, or image recognition. 

Phase your spending. Your prototype will generally be between $5,000 and $15,000, giving you an easy but valuable early assumptions test. Your Minimum Viable Product (MVP), which people can use, will generally cost between $15,000 and $50,000, depending on the complexity. Your future scaling costs will then hinge on your infrastructure, your users, and a number of features. 

If you want to understand how to efficiently invest your money and use Smarttek as your tech partner, you can design and develop AI software in a predicted budget and reach your goal in an optimal way. 

Case Studies and Real-World Examples 

AI is directly impacting multinational companies to make them smarter, faster, and more effective. The following three are widely recognized, real-world applications where AI has made it possible to deliver real value: 

1. Netflix – Smarter Content Recommendations

• • Problem: With its huge library of content, it is a challenge for the user to choose what to watch, which leads to decision fatigue, and the platform doesn’t get used as much as it could. 
  • Solution: Netflix uses AI-based recommendation systems to give content recommendations to every individual based on watch history, interest, and behavior. 
  • Tools used: Collaborative filtering, reinforcement learning algorithms, and internal machine learning models. 
  • Results: Over 80% of Netflix content viewed is driven by personalized recommendations, increasing user engagement as well as decreasing churn.
     

2. Toyota – AI That Empowers Factory Workers

• • Problem: Factory improvement was very dependent on engineers and outside consultants, slowing down speed and scope. 
  • Solution: Toyota developed an AI platform that does not require coding but allows line workers to create their own AI tools to improve processes. 
  • Tools used: Internal machine learning platform integrated within factory systems. 
  • Result: The project has freed up over 10,000 worker-hours per year and has led to over 40 plant teams attempting to automate on their own. 

3. Duolingo – Personalized Learning with AI

• • Problem: Manually creating educational material was labor-intensive, expensive, and unable to be scaled to an increasing user base. 
  • Solution: Duolingo utilized GPT-4 to develop real-time grammar recommendations, in-lesson roleplay dialogs, and customized tips. 
  • Tools used: GPT-4 via OpenAI API, implemented in Duolingo Max (one of their AI-based premium levels). 
  • Result: Faster creation of content, more individualized learning experiences, and another income stream with premium AI capabilities. 

Conclusion 

Developing AI software isn’t magic — it is a process: 

Find the problem → Prep your data → Train a model → Deploy it. 

You don’t need an enormous budget to get started. Even with a modest AI experiment, you can unlock concrete value.

So don’t delay: 

• Try a no-code tool; 
• Start a quick pilot; 
• Talk to an expert. 

Begin small, learn rapidly, and make something intelligent. 

Yuriy Nayda CTO, Managing Partner at SmartTek Solutions