Co-founder and CEO at 10 Senses
Recently, artificial intelligence (AI) has been on everyone’s lips. It is a widely discussed topic not only in the technology context but also among everyday people. It already has an impact on our lives and will continue to have an even greater one.
Along with the concept, AI-powered applications have emerged and, in recent weeks, truly caught on. This rise is mainly fueled by progress in machine learning (ML), natural language processing (NLP), or cloud computing.
As a result, worldwide businesses and developers are struggling to build cutting-edge AI solutions that outperform competitors. Such applications shall enhance productivity, automate complex tasks, or deliver intelligent user experiences.
Nevertheless, many of these apps lack a holistic approach during the planning and build phases. Consequently, they are underperforming, overly technical, and difficult for standard users to navigate due to their lack of user-friendly design.
In fact, to create high-performing, innovative AI apps, you need an appropriate technology stack that balances efficiency, scalability, and user-friendliness.
Once you read this article, you will know:
- What are the essential layers of the AI app technology stack,
- What each of the layers consists of,
- How to build each of these layers.
Essential layers of the AI app technology stack
A typical robust AI technology stack for high-quality apps consists of three main layers:
- backend infrastructure,
- AI programming
- user-centric interface.
Each of these elements plays a crucial role in delivering seamless user experiences, ensuring effective AI workflows, and data management.
Backend: managing data, infrastructure, and AI models
As you probably know, the backend is the foundation of all kinds of applications. It is responsible for handling everything from user authentications to data storage and AI model hosting.
Only a well-architectured and managed backend ensures reliability, security, and scalability.
The core concepts that should always be an integral part of the backend infrastructure include:
- User management and observability with user authentication and access control systems. Additionally, observability tools can assist in tracking performance, detecting issues, and optimizing AI workflows.
- Cloud and storage solutions with reliable cloud providers with scalable computing and data storage services, such as Azure, AWS, GCP, or Snowflake. When choosing the right provider, you need to take into account the AI services you need, compliance, security, or cost.
- AI model providers to cater to your use case. Since the AI ecosystem is dynamically changing, you should carefully observe the market and choose the right option. Some of the prominent ones include OpenAI, Gemini, Mistral, and Claude.
AI programming: building smart and efficient workflows
The second layer that is a core element of the cutting-edge AI app stack is AI programming. It is actually the heart of an intelligent application that determines how AI models interact with data, users, and other systems.
There are two critical elements of this layer, which are:
- Low-code for workflow AI that can streamline the development of AI workflows. Platforms that offer low-code, or even no-code, solutions enable rapid prototyping and deployments, which reduces the need for extensive manual coding.
- AI app analytics to efficiently analyze large amounts of data that an AI app generates. Integration with analytics tools assists in monitoring AI application performance, optimizing decision-making processes, and improving overall app reliability.
Interface: designing a user-centric experience
Finally, the last layer is the user-centric interface, which makes your AI application accessible and intuitive. Here, you need to take into account the complexity of your app and the level of required control to choose between:
- Ready-made tools that provide pre-built components that ensure consistency and ease of use. Such tools significantly accelerate development but can lack certain features or flexibility.
- Customizable tools to create tailored interfaces. If your app requires a unique design language or specific functionalities, you need UI tools like Figma that enable you to design and prototype interfaces according to the specific requirements.
All in all, a well-structured and complete AI app technology stack ensures that your app is scalable, efficient, user-friendly, and adaptable to dynamically changing technological possibilities. Therefore, keep in mind the three-layer stack with the right backend infrastructure, AI programming, and user-centric interface that all together allow you to build cutting-edge AI applications.
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