Imagine you’re a small business owner struggling to meet customer requests. Maybe you want to offer better support to your customers, but you don’t have enough time or money to solve the problem. You learn about AI and how it can help you tackle this issue. The only problem? You have no idea how to create an AI app. With the rapid rise of artificial intelligence, many entrepreneurs and developers are wondering how to build AI applications that can help them boost productivity and improve overall performance. In this article, we will walk through how to create an AI app from start to finish, including how to build a fully functional app without any coding skills.
One of the best ways to simplify the app-building process is to use Lamatic's Generative AI tech stack. This collection of beginner-friendly tools will help you achieve your objectives, like easily building a fully functional AI app from scratch without needing advanced technical skills.
What is an AI App?
Artificial intelligence refers to any software or machine that mimics human intelligence. Artificial intelligence algorithms power applications using AI and use them as one of their fundamental movers. The AI apps' possibilities are virtually limitless, and this is a transformative area of technology with broad usage. Such platforms, software, or apps apply AI to tasks requiring human input.
Artificial intelligence app development leverages various AI techniques, such as machine learning and natural language processing, for data analysis, pattern allocation, prediction, and interaction with people. Incorporating a rag pipeline into your AI application can significantly enhance its ability to manage and retrieve relevant information, making the app more efficient in handling complex data queries and providing accurate responses.
What Does AI Development Aim to Achieve?
The primary goals of AI software development are to augment human capabilities and automate processes. AI can be applied to provide intelligent solutions to problems. The best part is that artificial intelligence-based applications are designed to learn. They use data and can improve over time as they adapt to changes and determine how to better cater to users' preferences. This self-improvement allows them to deliver better results, making the technology more sophisticated.
Where are AI Apps Used?
Artificial intelligence can be used in lots of ways. People are getting more and more creative about tech startup ideas in the sector and how else to use AI's capabilities. In which industries is AI applied? Here are some domains where you can find AI apps:
- Healthcare Finance Business and legal services Logistics, supply chain, and manufacturing
- E-commerce and vendor management
- Education Construction SaaS (tools for marketing, human resources, customer support, analytics software, cybersecurity, and so on)
- Transportation (e.g., autonomous vehicles and delivery drones)
- Environmental science
These are just some of the spheres where artificial intelligence can bring value. What solutions are already available?
- Digital voice assistants and smart virtual assistants such as Siri
- Speech recognition and smart speakers like Alexa on Amazon Echo
- Chatbots and personal tutors like Elsa
- Biometric tools like face or fingerprint recognition
- Image generation tools such as Midjourney and editing tools
- Text generation and language translation tools
- Augmented and virtual reality (for gaming, virtual try-on, virtual reality for deaf, and other use cases)
- Music creation tools
- Sales acceleration solutions (e.g., personalized shopping suggestions)
- Plenty of other solutions
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How to Create an AI App from Scratch
The first step in building an AI app is designing the AI model. Let’s review the steps required for this part.
Define the Problem You’re Solving
The first step in building an AI-powered app is to define the problem your app will solve and
determine if AI is the best solution. This will require a thorough understanding of the problem domain and knowledge of the various AI techniques and algorithms that can be applied to the problem.
The choice of AI technique will depend on the nature of the problem, the type and volume of data available, and your desired performance metrics. Frame the issue in a way that allows for creative solutions and the exploration of multiple approaches - for example, a data-driven approach to problem-solving or using techniques such as reinforcement learning or deep learning to discover patterns and insights in the data.
What Do You Want AI to Do?
Once the problem is defined, narrow down what you want AI to do to get closer to solving your issue. For example, consider the following sample requests for an e-commerce application that aims to reduce cart abandonment:
- Estimate sales figures for the next period
- Project ROI over the next five years
- Learn why customers abandon carts
- See which services attract the most customers
The ideal task for AI would be to find out what’s behind outcomes (impact analysis). Before developing your AI application, you can further define your goals (e.g., understanding why customers abandon carts).
Collect the Data and Choose the Algorithm
Where can you find it, and how can you prepare data for AI?
AI application type
Best data sources
Preparation techniques
Forecasting
Public data sources (e.g., Eurostat, Google Finance, NASA)
Detrending, normalization, or transformation
Impact analysis
Institutional data sources (e.g., WHO, government reports, research institution websites)
Matching data to time frames or conditions, normalization, ensuring causality
Clustering
Multimedia data repositories (e.g., UCI Machine Learning Repository, Kaggle, ImageNet)
Scaling, normalization, and identifying and removing irrelevant features
Classification
Supervised learning data sources (e.g., Kaggle, Amazon’s AWS datasets, social media APIs)
Bias elimination, handling missing values, encoding categorical variables, augmenting
Association
Retail and e-commerce transactional data, public datasets on consumer behavior
Structuring in a specific format
Anomaly detection
Network traffic and security datasets (e.g., KDD Cup), industrial and machinery maintenance data repositories
Normalization, handling outliers
Preparing High-Quality Datasets for AI Model Training and Validation
Collecting and organizing relevant data for your AI model will require acquiring and preprocessing large volumes of data, which may come from various sources and in multiple formats. You should use specialized techniques such as data augmentation, normalization, and feature engineering. You might collect data from multiple sources or generate synthetic data using generative adversarial networks (GANs) techniques. The goal is to collect and prepare a high-quality dataset that can be used to train and validate the AI model.
Choosing the Right Machine Learning Algorithm for Complex Problem Solving
You must choose the algorithm that best suits your problem - supervised, unsupervised, or reinforcement learning. The chosen algorithm should be capable of generating diverse and varied outputs that can capture all the nuances and complexities of the domain. This part may also require specialized algorithms such as recurrent neural networks (RNNs) or attention mechanisms that can handle variable-length inputs and generate outputs that vary in length and complexity.
Train Your AI Model with Data
The next step in building an AI-powered app is to train and optimize the AI model using the selected algorithm and the prepared dataset. You’ll need to feed the dataset into the algorithm and adjust its parameters to minimize the error or loss function, which measures the difference between the predicted and actual outputs.
Design the training process carefully to promote exploration and variability in the model's outputs while maintaining accuracy and consistency. You may be required to use techniques such as dropout, early stopping, or adversarial training to prevent the model from becoming too rigid or biased.
Choose the Right Model and Tools
Your project’s nature and size will guide your choice between custom AI development using a framework or opting for a pre-trained, cloud-based AI/ML model available through an API.
Custom AI Application Development
This approach is best suited for projects requiring specific model architectures or when you plan to train models from scratch with your data. In other words, it’s suitable when you fully understand how to develop an AI app and need detailed control, including design, training, data preparation, and evaluation.
To develop a custom AI application, you’ll need a solid grasp of machine learning algorithms, data science, and possibly deep learning.
Cloud-Based AI/ML Model
This option is great for projects that need to integrate AI features quickly and with minimal setups, such as chatbots, image and speech analysis, and language translation services.
You can select a cloud model from providers like:
- OpenAI
- Google Cloud Vision
- IBM Watson
And use their API to add artificial intelligence capabilities to your application. This method allows you to add AI features to applications without understanding the complex model structures or managing the computing power for AI model training and operations. Basic web programming knowledge and the ability to use external APIs are all required.
Which Programming Languages are Optimal?
There are many approaches to developing an AI app from a technical perspective. You have options here:
- NLP libraries exist in some programming languages, such as the Natural Language Toolkit (NLTK) in Python. Mentioning a few:
- AI frameworks
- Google AutoML
- TensorFlow
- PyTorch
These are quite popular options.
- You can also consider platforms with ready-made stuff for your AI app development, like Amazon's AWS machine learning models, Google's platform with an AI hub and building blocks, or Microsoft Azure's built-in AI capabilities and machine learning services.
Applying these can save much time on work that would otherwise take much time to handle from the ground up. Moreover, choosing to favor cloud-based infrastructure can also make you more flexible in the long run.
Build the MVP and Train the AI
The actionable step of creating an AI application. Entrepreneurs commonly choose the path of a minimum viable product (MVP) instead of enrolling in a large-scale project. Such iterative development provides the flexibility to gradually build your product from its smallest, earliest version, improving it as you go. You work on information architecture. It is considered a best practice to opt for modular architectures, as they usually enforce the app's scalability. Teams also create the MVP design, which is followed by feature development and security enhancement. You should add embeddings or other functionality based on the peculiarities of the built app.
The team also creates algorithms to train AI by using model learning. The optimal approach to apply (unsupervised, supervised, or reinforcement learning) depends on your solution's specifics and goals. To train the model, over time, you'll need to feed it the data you've prepared, ensure the parameters work as intended, and tweak them. Then, test the model's performance to evaluate how well it works, possibly falling back on the KPIs and metrics you chose earlier. The model gets trained and adjusted until it delivers the expected results.
AI Integration and Testing
When developing an AI app, the solution is thoroughly QA-tested for performance. If it's ready to go, the team integrates the created and trained AI into the system's front-end or back-end, often using APIs.
Conducting unit tests, integration tests, and user acceptance tests are common. Model testing isn't a one-time task; it will be refined continuously over time. This is why it is common for teams to implement CI/CD pipelines, which make it simpler to maintain the apps and run tests. Developers also usually link up a feedback loop to allow users to help the AI system improve.
Release and Improvements
If everything works well, it's time for release and MVP launch. What happens afterward? The MVP stage includes revision, fixing any issues, optimizing performance, and improving the solution. This includes adding new features in consequent sprints or expanding the existing functionality that is next in the plan.
Lamatic: Your All-in-One Generative AI Platform
Lamatic offers a comprehensive Generative AI tech stack that empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our generative AI tech stack.
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19 Best AI App Builders
Here’s a list of AI app builders that can help you create AI-driven applications. First, we'll look at the best available options, then break down what makes a good generative AI app builder.
Tool
Best for
Standout features
Pricing
Lamatic
Comprehensive Generative AI Tech Stack
Managed GenAI Middleware, Custom GenAI API (GraphQL), Edge deployment, GenOps, Integrated Vector Database (Weaviate)
Free plan available; start building GenAI apps today
Softr
Ease of use and speed
Rapid app generation from prompts
Free plan available; paid plans start at $59/month
Microsoft PowerApps
Creating and editing with AI
Real-time editing with AI
Starts at $20/user/month
Google AppSheet
Turning spreadsheets into apps
Automatic app generation from spreadsheets
Starts at $5/user/month (Google Workspace subscription required for AI prompts)
Quickbase
Building enterprise-grade apps
Advanced data governance
Starts at $35/user/month for a minimum of 20 users
Pico
Building an app only with prompts
Extensive customization
Starts at $29/month
Construct
Building simple tools
Minimalistic app design
Free in beta; no pricing information available
Create
Building with a single prompt
Use one prompt to build an entire app
Starts at $99/month
Best Community
No-code visual programming, drag-and-drop interface, supports mobile apps and web applications
Free plan available; paid plans start at $25/month
Glide
Best spreadsheet integration
Turns spreadsheets into apps, real-time updates, offline capability, push notifications
Free plan available; paid plans start at $25/month
Naologic
Best user interface
100+ app templates, no hard limits on traffic or storage, easy workspace customization
Free plan available; paid plans start at $199/month
Backendless
Best for backend management
External database connectivity, user authentication, real-time chat, geolocation, push notifications
Free plan available; paid plans start at $25/month
Adalo
Best for ease of use
Drag-and-drop UI components, built-in database, Zapier integration, customizable design elements
Free plan available; paid plans start at $50/month
Thunkable
Best for rapid prototyping
Drag-and-drop interface, functional mockups, custom mobile app experiences, collaboration features
Free plan available; paid plans start at $13/month
Swiftspeed
Best for website-to-app conversion
AI-powered, website-to-app conversion, extensive customization, comprehensive analytics dashboard
Free plan available; paid plans start at £10/month
Builder AI
Best for custom apps
Native iOS/Android app development, aftercare warranty, free cloud hosting, pre-packaged or custom app options
Custom pricing is available; packages start at $10K
App My Site
Best for website-to-mobile app conversion
Converts websites (WordPress, WooCommerce, etc.) to native apps, real-time sync, customizable apps
Plans start at $3/month
Mobiroller
Best for small businesses
Self-service platform, multilingual support, collaboration tools, process/workflow automation
Free plan available; paid plans start at $10/month
Generative AI App Builders vs. Building AI Features Into Apps
Generative AI app builders and features for integrating AI into existing apps are built for different goals, and each process is different. You can use a generative AI app builder to build an app from scratch with AI. These tools let you use AI to accelerate the app creation process, and they generate the app’s code based on your prompts.
As you build the app, you can customize its AI features. You can tell the system to create a chatbot, and it will generate the code for the bot and help you customize its personality. You can even deploy the bot to your app when you’re finished.
Accelerating App Development with Generative AI Tools
Using a generative AI app builder can help you get to the finish line faster, but you will still need to test the app, refine its features, and customize its UI for your users. For those looking to build apps with AI features, say, you want to develop an app that summarizes text or turns it into audio. There are other options you can look at.
You can build your chatbots with Zapier's AI Chatbot tool. Start by telling it what to do, connect knowledge sources to ground it on your data, and share it with others. It's great for implementing robust internal tools fast. OpenAI's GPTs offer similar possibilities. You can customize ChatGPT for unique cases, such as creative writing, handling negotiations, or anything else you can come up with.
Creating Custom AI-Powered Apps with No-Code Builders and API Integrations
One step more complex, there's Glide. It's a no-code app builder that offers AI-powered interface components. When you add them to your app, you can generate text, extract text from images, or render audio into text. If you want to create a unique app with custom AI features, you can do so with any app builder supporting API connections.
Choose your AI model provider like:
- OpenAI
- Anthropic
- Hugging Face
Community model. Get the API keys and plug them into the app editor. Then, set up the calls and how they're received on your app. This option is more advanced, but you can create any AI-powered app with complete freedom.
Start Building GenAI Apps for Free Today with Our Generative AI Tech Stack
Lamatic offers a sophisticated managed middleware solution that seamlessly integrates with existing systems and APIs to accelerate the development and deployment of GenAI applications. It provides the tools to build custom GenAI APIs and deploy them in a secure, production-grade environment to facilitate rapid application development.
Custom GenAI APIs Built for Speed
Lamatic helps you get up and running quickly by automating complex processes to eliminate tech debt and accelerate custom Generative AI application development. Our solution provides a fully managed GenAI middleware that helps teams:
- Integrate AI functionality into their existing systems and applications
- Build new GenAI applications
- Deploy them in production-grade environments
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