ben brown

September 4, 2023

Business

Best AI Cross-platform Development Frameworks

ben brown

September 4, 2023

Business

It’s a lot more than task automation with AI in cross-platform development. For the frameworks that leverage it, developers can develop versatile - efficient applications. The popularity of cross-platform app development stems from its unique advantages. As its influence grows, so do tools and frameworks for cross-platform development in the market. This surge initially happened gradually and then suddenly, tons of mobile app development companies became eager to explore what it could do for them.

The Trajectory of Cross-Platform Apps

Internet service started in the 1980s. Back then, you needed a PC to access the web. But as technology developed, mobility became one of the most important aspects of the user experience. Users started to experience the temptation to use the internet while on the go. This impulse was increased by the introduction of smartphones and tablets in 2012. Today, your phone provides 100x the experience a PC would have back then.

According to Statista's analysis of internet accessibility, mobile users account for more than 55% of all online traffic. This information was sufficient to support a change in the way native mobile apps are developed. Mobile app development and mobile apps started to take precedence over other factors. The original response was to create distinct desktop and mobile applications. However, it didn't take long for developers to realize that this approach was not only lengthy but also expensive. As a result, cross-platform app development gained momentum.

Enter AI

AI’s role in cross-platform development frameworks is consistent when it comes to developing apps and optimizing their performance. But the true potential of AI becomes most apparent when it's integrated into the development cycle. AI in cross-platform development isn't just about keeping up with the trend; it's about pushing the boundaries of what's achievable. In the development cycle, AI does a few things; fine-tunes algorithms, automates coding tasks, predicts user behavior, analyzes data, and personalizes the user experience.

To get the most out of these features, developers need the right framework and AI/ML expertise. With that said, here are some ideal options:

For integrating AI:

TensorFlow: Best suited for tasks like image or speech recognition, natural language processing, and more complex AI tasks.

PyTorch: Ideal for developing AI-driven features within cross-platform apps. Its got a dynamic computation graph and user-friendly interface which is best for tasks demanding neural network implementation.

Keras: A high-level neural networks API that simplifies AI model integration. Great for tasks like image classification, text generation, and recommendation systems.

For Cross-Platform Development with AI:

React Native: Excellent for delivering consistent, AI-enhanced user experiences across iOS, Android, and the web.

Flutter: Offers a single codebase approach and customizable UI components, which is ideal for creating visually appealing, AI-powered cross-platform apps that prioritize performance.

Xamarin: Focuses on code reusability and allows AI functionalities in cross-platform apps. Good for developers with C# skills aiming for a smooth development experience.

For User-Friendly AI Integration:

Ionic: Great for creating aesthetic mobile apps with integrated AI features for iOS and Android.

Kotlin Multiplatform Mobile: Facilitates code sharing across iOS and Android platforms, great for tasks requiring consistent AI features across devices with native-like performance.

The Age of Personalization

Many brands struggle with personalized customer experiences, which was evident in a Gartner report that revealed the struggle in 63% of digital marketing teams. Interestingly enough, less than 20% of them have used AI or ML throughout their operations.

What machine learning does in the context of cross-platform development is make software that not only accommodates user choices but also adapts to and picks up on user behaviour. Machine learning makes judgements based on data analysis. It anticipates user behaviour and fine-tunes algorithms to maintain a customized user experience.

Again, tools like TensorFlow, PyTorch, and Keras are great because they enable features like natural language processing and predictive analytics. These technologies allow programmers to produce dynamic and adaptable applications that take into account user preferences and actions, ushering in a new era in which software not only meets user demands but also develops along with them. Machine learning is the future of cross-platform development, its ability to completely change the user experience is enough to lock that claim in for most developers.

Moving Forward

This year, mobile apps are projected to do almost a trillion dollars in revenue. With that in mind, it begs the question; What frameworks should you choose? The simple reply is that it depends on the company and the functional specifications of the app. If you want the best advice, reach out to a qualified and reputable provider who will not only put you on a path to success but also limit the headaches of getting started.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

ben brown

August 28, 2023

Business

Top 7 Best Java Build Tools

ben brown

August 28, 2023

Business

Cutting down workflows is the name of the game in the world of custom software development. Build tools are great for B2B companies in this case since they handle the process of creating, compiling, and managing code. Yet, it’s no easy task to find a match between the features of tools and the particular demands of a project.

Nonetheless, streamlining workflows during development heavily relies on Java build tools - being aware of what they can do and how they can be integrated will set you up for success. By automating the Java application creation process through source code, managing dependencies, and handling bytecode, developers can reduce the likelihood of errors in addition to labour.

This mechanized coding process also lets developers create unit and integration tests, which expands code coverage analysis. Additionally, it aids in packaging and distributing Java applications, thereby streamlining the software deployment process as a whole.

Java Build Tools Explained

A build tool is a utility that operates through command-line instructions while automating the process of building applications systematically. Within the Java context, these tools turn code files into bytecode and then package them into executable files or resource libraries.

Having build tools simplifies the process of managing dependencies for enterprises undergoing Java development. By automating tasks, these tools reduce the chances of errors. This, in turn, creates space for the implementation of testing protocols before the deployment of the actual product. The reduction in potential complications augments the likelihood of the final product meeting the criteria set by the client or end-user, thereby fostering confidence in the development team.

What to Look For When Choosing a Tool

When you’re looking for a Java build tool, there are several factors to consider. Personal preference is of course important, but it's best to focus on the complexity of the project, community support and adoption, integration with other tools, and even consider the learning curve for Java developers.

For smaller-volume projects with simple requirements, lightweight tools like Apache Ant can be helpful. However, multiple-module projects that you want to scale up or down include more dependencies and customization needs that require tools with more advanced features. So to put it simply; look for a build tool that offers extensibility and customization with additional plugin features that allow for further customization to meet the specific needs of a project.

Always do your research beforehand, especially when it comes to the scope of the project and how libraries will contribute to performance, structure, or scalability. General libraries, like Boost, STL, or Qt, take more time to research, especially if you need a highly specific function. You’re more likely to find these types of features in libraries meant for particular purposes, such as logging, HTTP, MXL, and connection pools.

A mark of a good tool is best seen in its integration with other tools and the control system version. Through the continual integration of modern Java build tools, there are standard practices that can enhance the development, testing, and deployment of an application, ensuring that any code changes are rapidly delivered and integrated smoothly.

The learning curve for Java can be steep, so always look at starting with tools that are beginner-friendly, like Apache Ant. It will help you, as a developer, gain a practical understanding of the working schematics, while a tool like Maven requires more experience.

After mastering the basics of Java, make a list of steps for completing just one simple project. Create a map or list that will help you visualize the process. Each step will demand a varying amount of research, so think of a way to keep track of resources and tips. Find online forums with members who have experience and expertise with Java.

Top 7 Java Build Tools

Gradle: Gradle stands as a widely embraced build tool that uses a Groovy-based domain-specific language (DSL) for scripting. Its flexibility is a big draw, and it makes it useful during the construction of diverse software projects.
Apache Maven: Maven, another popular build tool, places a strong emphasis on convention rather than configuration. With an XML-based configuration file, it manages project dependencies and construction processes.
Apache Ant: Despite its age, Apache Ant is still widely utilized as a build tool. It also uses an XML-based configuration file and is super customizable which are both key features that persist in its popularity.
Bazel: Bazel is meant for expansive, multi-language ventures, and is as a purpose-built build tool that’s highly scalable.
SBT: Engineered explicitly for Scala projects, SBT is a dedicated build tool. The utility relies on a Scala-based DSL for scripting, and its configurability sets it apart.
Dragonboat: Dragonboat is a high-performance multi-group Raft library in Go. With its feature-complete implementation of the Raft consensus algorithm, it has a strong foundation for constructing distributed systems that demand consistency.
Go-Zero: Go-Zero offers a web and RPC framework designed for crafting high-performance and resilient services. It’s a comprehensive solution that encompasses a productivity-enhancing code generation tool named goctl, which elevates its efficiency.

Tool Comparison

The general features and use cases of these tools are something developers should absolutely be aware of. We’ll use Gradle as a point of reference, this is a brief look at how they compare and contrast:

Gradle vs. Apache Maven:

Gradle uses a Groovy-based DSL for scripting, while Maven relies on XML configuration.
Gradle's flexibility is good for diverse projects, whereas Maven's emphasis on convention specifically simplifies setup.
Gradle's learning curve might be steeper because of its scripting language, while Maven's XML configuration is a bit more approachable.

Gradle vs. Apache Ant:

Both Gradle and Ant use XML-based configuration, but Gradle's DSL provides more expressiveness.
Gradle's adaptability suits varied projects, whereas Ant's customization options are key to its popularity.
Gradle often requires less manual XML coding than Ant.

Gradle vs. Bazel:

Gradle is versatile and Groovy-based, while Bazel excels in scalability for large, multi-language projects.
Gradle's syntax might be more user-friendly for anyone accustomed to scripting languages.
Bazel's strengths lie in distributed caching and parallelized builds, making it better suited for more complex projects.

Gradle vs. SBT:

Gradle's Groovy-based DSL contrasts with SBT's Scala-based scripting.
SBT is optimized for Scala projects, while Gradle's flexibility accommodates a broader range of languages.
Gradle's learning curve might be gentler because of its scripting familiarity for many developers.

Gradle vs. Dragonboat:

Gradle is a build tool, while Dragonboat is a high-performance Raft library.
Gradle focuses on automating build processes, whereas Dragonboat is used for distributed system consistency.
Comparing the two is like comparing construction tools to foundational components for distributed systems.

Gradle vs. Go-Zero:

Gradle is a general build tool, while Go-Zero is a web/RPC framework.
Gradle handles software construction, whereas Go-Zero specializes in high-performance, resilient services.

The Comparison to Gradle is simply because it’s super well-known and widely used within the software development community.

The Takeaway

It can’t be stressed enough that choosing the best build automation tool hinges largely on how well it aligns with the prerequisites of your Java project. Preliminary research is fundamental here.

Having a project management tool like Trello, Jira, Asana, or Basecamp can also help out when it comes to tracking your project and keeping it organized. If your project is something you think could benefit from expert help, we’re ready to answer any questions you may have!

Written By Ben Brown

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ben brown

August 25, 2023

Business

How AI Consultancy Works in Manufacturing

ben brown

August 25, 2023

Business

Manufacturing is a very broad term when you look at how the industry is defined; “any industry that makes products from raw materials by the use of manual labour or machinery and that is usually carried out systematically with a division of labour”. Nonetheless, this is a space that benefits from automation arguably more so than any other industry. That’s another subject to get into but essentially, time, money, and efficiency is where the appeal to automation stems from.

A company on its own can’t just look for the metaphorical “band-aid” to put over its current processes and magically scale performance. For this reason, they outsource to consultants; a team that can see not just the current shortcomings but ones the company can expect down the line.

What we as vendors know about manufacturing companies in North America in 2023, is that procurement and sourcing, as well as supply chain disruptions, are among the critical challenges faced. Artificial intelligence is a great resource to overcome these challenges, but the specifics that an AI system needs to address in the context of manufacturing are intricate and multifaceted.

Solving Problems Across Industry Models

As we’ve already covered, the industry is broad and every business will have different outcomes they’re looking for. With that said, here are some practical application examples that can be leveraged across multiple business models:

AI-Powered Procurement Platforms: By analyzing information from supplier databases, market trends, historical data, and other relevant data sources, AI procurement platforms can match specific procurement requirements with supplier capabilities and qualifications. Resources for this include Globality, Zycus, and JAGGAER.

Spend Analysis: AI can analyze spending patterns and identify opportunities to save money by scrutinizing large volumes of procurement data. It also offers insights into spending behaviour, supplier performance, and contract compliance. Some platforms for this could be SpendHQ or SAP Ariba.

Supplier Risk Management: These AI tools can assess supplier risk by analyzing various data sources, which might include financial data, news articles, and social media. They’ll help identify potential risks and enable proactive risk mitigation strategies. Platforms for this might include Resilinc and Zycus iContract.

Contract Management: Platforms like Icertis or Evisort can automate contract management processes by extracting key information from contracts, identifying risks and opportunities, and ensuring compliance with contractual terms and conditions.

Demand Forecasting: This is simply for optimizing inventory levels, reducing stockouts, and enhancing the efficiency of your supply chain overall. For this, you’ll want to look at BlueYonder.

Where AI Consulting Comes In

Consulting at a base level is about creativity, bringing an outsider perspective into your organization with expertise in problem-solving to create solutions that will optimize your business long term, great. With AI in the picture, this process looks the same, but even more promising - specifically when it comes to data management. Whether you’re a small-scale manufacturer or a large industrial player, data is at the heart of your operations and it can be extremely tedious and time-consuming.

Most companies with in-house development teams may either be trying to navigate multiple priorities or simply might not have the expertise to construct a system that can manage data using machine learning and AI. For a company this is a problem, for a consultancy this is a massive opportunity. When you say the words “trouble managing data”, a consultancy correlates wasted time and resources, which would push them to create a roadmap looking something like this:

Know the Plan: The consultants start by understanding what needs to be done. They figure out what problems and good things we want to achieve with your information.

Make a Smart Plan: Next, they think about how to do it in a smart way that matches your needs. They decide what kinds of information we need and how to keep it safe.

Choose the Right Rules: They make rules like a game. They say where the information comes from, what it looks like, and how to take care of it. This helps everyone use the information the right way.

Think about AI's Job: They talk about how AI can help. They make sure the AI does what you want and is safe.

Stay Safe: They check to make sure the AI is working well and doesn't do anything bad. They also make rules to stop the AI from doing bad things.

Keep Everything Nice: They make sure the information stays good and not messy. They also make sure it's always correct and up-to-date.

Make It Even Better: They look at how things are going and think about how to make them even better. They use the plan to keep improving how you take care of your information.

The System

At the core of AI's prowess lies its ability to learn. Think of it as a virtual apprentice that gets better with experience. Initially, AI algorithms are fed historical data - production records, machine performance, and even external factors like market trends. Through sophisticated techniques like machine learning, AI identifies hidden relationships between variables and develops predictive models.

An ERP system infused with AI is the ultimate vision for manufacturing companies. For anyone who’s never imagined that, it’d look something like this: As raw materials flow into the factory, embedded sensors send real-time data to the AI-powered ERP system. The AI, like an orchestra conductor, processes this influx of information. It then predicts maintenance requirements for the machines before they break down, optimizes production schedules based on market demand, and even fine-tunes the supply chain to minimize delays.

Too good to be true? We think not.

The Takeaway

AI consultancy isn’t so much about thinking up and designing systems as it is about giving companies the best shot at success for the least amount of risk. It’s not an easy process, but that’s what experts are for. We’d love to make your problems our solutions, so be sure to bring us all of them.

Written By Ben Brown

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