The global artificial intelligence (AI) market staggeringly shot up to one hundred and eighty-four billion dollars in 2024 compared to the previous year, various sources of market trend statistical analysis write. The AI market is projected to exceed eight hundred and twenty-six billion dollars by 2030.
The huge increase in computing power makes it possible to use artificial intelligence for commercial purposes.
What Benefits Do Customers Get from AI Solutions?
If SaaS startups lack in-house professionals for AI development, they may seek a reliable outsourcing company, for example, Belitsoft, to get a range of AI software development services.
AI Integration via API Module
AI development specialists create customized AI solutions that allow users to get access to real-time analytics and insights for inventory management, financial forecasting, and so on.
Programmers develop modules for code-free integration with various data sources in the customer’s infrastructure (e.g., financial app, or ERP system).
Clients use artificial intelligence solutions that are trained specifically on their company’s info, for example, transaction history, real-time financial data, etc.
Strict adherence to standards and robust security measures (data poisoning, adversarial attacks, role-based and attribute-based access control, etc.) ensure regulatory compliance and data security.
Analytical AI System Creation
It shifts through large sets of data and finds complex patterns to detect and anticipate the preferences of the users.
Cutting-edge machine learning (ML) models allow customers to analyze data trends in real-time. It’s about data clustering for precise and quick categorization of data and stream processing of big data volumes.
AI-driven marketing helps customers analyze interactions and preferences smartly via computer vision, time series, and ensemble models. As a result, they can reduce churn and increase earnings.
Via machine learning models, developers automate email segmentation. The customer team is free for strategic initiatives, while AI solutions prioritize and generate responses to standard inquiries without human control.
AI Chatbots Building
Customers are less reliant on human agents and may improve response accuracy and speed by applying an AI assistant for conversations. The chatbot understands the context of any user and is able to process multi-intent, complex queries.
Voice bots easily interact with customers thanks to natural language processing (NLP) models and advanced speech recognition.
It uses a customer segmentation ML model and acts as a personal advisor to send customized messages that help to retain clients and increase their long-term spending.
The chatbot skillfully engages customers in cross-channel communication that runs smoothly. It reduces redundancy and maintains the context, and users can switch without any effort.
Which Language Is Best for AI Development?
Talking about the best languages for AI development, attention is paid to the following specific points:
- Readability and Ease of Use. In AI development, there is often rapid prototyping, experimentation, and iterative improvements. An easy-to-read and easy-to-write programming language reduces the possibility of errors and speeds up development.
- AI-focused Libraries and Ecosystems. Libraries, pre-built tools, and a rich ecosystem save effort and time for data preprocessing, model training, and evaluation.
- Speed and Performance. These characteristics are important for computationally intensive AI tasks like large-scale data processing and deep learning (DL).
- Community and Support. The larger and more active the community, the better the programming language’s troubleshooting support, tutorials, and documentation, and the more new libraries and tools are available.
- Scalability. AI applications can serve a large number of users simultaneously and process large data sets.
- Use Case Specificity. Computer vision (CV), natural language processing (NLP), and other AI tasks may have specific requirements that are loyal only to certain languages.
- Learning Curve. If teams have different levels of expertise, spending a lot of time and effort learning a language risks reducing productivity.
- Deployment and Production. Once developers have created prototypes, moving to production is best done with a robust, high-performance programming language.
- Trends and Long-Term Stability. Developers who choose a language with a growing ecosystem and a mature community gain access to modern tools and long-term support.
There is no single best programming language for developing AI solutions, but there are several popular options that are best suited for processing big data.
Python
Python was developed long before AI became popular among enterprises, but today this programming language is often used for AI development.
It is a dynamically typed language, so the programmer does not need to specify types every time and can write faster. Python itself checks types during runtime. This programming language has easy-to-read code, so most programmers find Python the easiest language to get started with. In addition, it receives wide support from communities in the field of AI development (ML, DL, CV, etc.), since it is an open-source language. It’s also strongly supported in medicine, bioinformatics, and health informatics fields.
As a subset of artificial intelligence, Python is most often used for ML. Its demand for AI development is explained by the fact that it is a data analysis tool that is popular in the field of big data and analytics. Data analysis work is carried out efficiently thanks to a rich ecosystem of libraries such as TensorFlow, Keras, OpenCV, SpaCy, scikit-learn, and others.
Specialists who work with AI, design and carry out DL architectures with various Python frameworks, tweak generative models for specific industry tasks, and create custom ML systems. They also develop feature engineering pipelines (processing data, extracting features, and transforming to optimize model performance).
Python has a high-level framework called Django that enables SaaS startups to build robust AI-integrated products. It enables developers to quickly create APIs for AI servers and deploy ML models.
Python enables workflow automation and secure application deployment in cloud environments (e.g., Azure, AWS, GCP). This language, along with others, is used to secure Large Language Model architectures, artificial intelligence, and machine learning. With Python, developers eliminate the top ten OWASP vulnerabilities and implement WAP configurations and API security in web-based SaaS solutions.
Java
This language is an open-source, multi-paradigm programming language with object-oriented features. Software engineers use it for both artificial intelligence and machine learning. There is a Java Machine Learning Library that includes machine learning algorithms implemented in Java.
Programmers use a long list of libraries such as Deeplearning4j, Apache OpenNLP, RapidMiner, Apache Jena, etc. for AI development. They build neural networks, machine learning algorithms, and search algorithms using these tools. Except for extensive libraries, Java has a strong typing system.
C++
It is a popular object-oriented programming language with extreme flexibility, which makes it ideal for resource-intensive applications. Developers use С++ for multi-platform and multi-device application creation.
Low-level C++ provides superior AI model management. Even though AI experts may not choose this language primarily because of the steep learning curve and verbosity, ML/DL libraries such as TensorFlow, mlpack, Caffe, and others are built in C++.
AI-based apps like robotics and autonomous systems that require fast and efficient real-time processing are developed in C++.
C++ programming focuses on high usability and safety of code. Since it’s compiled, it’s the most compatible with C and software engineers can implement it on various platforms.
R
R is a free, open-source programming language. It supports statistical computing as well as data plotting. AI programs are mostly constructed on the foundation of statistical models of large sizes, and R’s functions and packages, such as nonlinear and linear modeling, clustering, time series analysis, and classification, are useful to construct them.
This language supports effective data visualization and analysis, thereby allowing developers to easily detect data dependencies and construct models. R has a vast community that develops specific applications and new packages of this language for AI use.
Julia
It is one of the young programming languages that is optimized for scientific and numerical use. Julia is at the midpoint between the easy-to-read and clean syntax of Python and the high performance of C++. Developers can create optimized code that remains structured and easy to work with, i.e., its modularity is not compromised.
Many AI operations, especially DL, often involve numerical tasks. Since this programming language is developed for scientific computing, it is well suited for these tasks.
Julia’s functionality in the AI area is further enhanced by numerous new frameworks and libraries designed specifically for AI tasks (e.g., JuliaAI, Flux).
Haskell
Statically-typed language with a lot of features useful for AI development. The Lazy Evaluation feature allows programmers to define unlimited data structures.
Among the benefits is that developers can describe algorithms precisely and clearly. Haskell is suitable for ML, offers type safety and offers parallel processing on multiple cores.
Scala
This language combines functional and object-oriented programming styles. This allows mathematical clarity through immutability and pattern matching and modular data abstraction via methods and classes.
Scala’s advanced type system provides static type checking, ensuring robustness for scaling. It also uses inference for flexibility. AI workloads are distributed across a parallel infrastructure thanks to asynchronous processes.
Specialty libraries provide distributed ML on Spark, numerics (Breeze), neural networks (ScalNet), and interoperation with Java ecosystems (DeepLearning4J).
Developers can deploy ML in production with high performance, build streaming analytics pipelines, and serve models in real-time using Scala.
The language’s integration with big data ecosystems (Spark) accelerates the mathematical transformations that underlie multiple ML methods. Scala also unifies AI applications that are ready for DevOps and scalable within a safe single language.
How Can a Software Development Company Assist?
An outsourcing company Belitsoft has over 20 years of expertise in developing and integrating reliable software. SaaS startup owners from industries such as healthcare, automotive, finance, and so forth, rely on Belitsoft’s deep tech knowledge in AI and ML, including TensorFlow, PyTorch, and specialized frameworks for NLP and LLM. They also benefit from the professionals’ experience in incorporating any ML model, from predictive and segmentation models to statistical ones.
AI SaaS startups also can get comprehensive assistance in building custom APIs, MVP creation, cloud migration, and other software development services. An experienced team adapts its services to the specifics of each business. They provide customers with Agile methodology following and enable them to release new app versions every few weeks. The Belitsoft software development company offers:
- Client data gathering from different sources in the staging database via continuous streams, batch processing tools, and direct API connections.
- A separate warehouse for AI analysis data.
- Implementing security protocols for compliance with PCI, GDPR, HIPAA, and other standards related to the client business’s industry.
- Carrying out data encryption and multi-factor authentication for extra protection of customers’ sensitive info.
If you are looking for reliable assistance in developing an AI system that will grow according to your needs, manage complex tasks and data, and be flexible enough for deployment either on-premises or in the cloud, you can contact Belitsoft professionals.
