Every industry is adopting AI, presenting new solutions to problems and opportunities for more intelligent decision-making. However, organizations that operate on existing application frameworks may ask, “Is it possible to integrate AI with our .legacy .NET system without ripping everything apart?” The answer to that is absolutely yes.
In this blog we will provide information on how to add AI features into legacy .NET software. We will guide you through the steps, whether it is about adding machine learning, natural language processing, or predictive analysis. Suppose you are a business planning to reach out to .NET development or looking for quality .NET development services. In that case, this article will prepare you on what questions to bring forth to prospective vendors.
The why is equally as important as the how, so let’s explain the purpose behind blending AI with net legacy systems. Below are some of the most noteworthy pros:
To better understand if AI is the right fit for your .NET applications, check out our detailed guide!
Integrating AI features into a legacy application is not as difficult as it seems, but a good deal of groundwork needs to be done in advance. This is what you need to do before starting:
Get to know the architecture and the dependencies of your existing .NET application, as well as its performance constraints. Check if the application is using older technologies such as the .NET Framework or if it has been upgraded to newer counterparts like .NET Core or .NET 6.
In order to facilitate AI features integration, older implementations might need to be restructured or even migrated to newer .NET versions. It may be beneficial to engage a Dot Net development company early in the process to assess your application’s obsolescence and work towards solutions.
What industry is your organization operating in? What are some industry standards? Understand what needs to be done for an application/service to operate with AI. Here are a few critical use cases to keep in consideration:
Your strategy for incorporating AI tools should be based on the requirements of users alongside your organisational objectives.
Having a legacy .NET system is a good indicator for having data services that funnel clean and structured data. AI depends heavily on data services, so consider having data services or modernising the current database.
The existing setup might not support the computing resources required by algorithms, which is usually the case. Make sure the application is on Azure Cloud, which has scalability and AI readiness. It supports .NET infrastructural development.
Prepare the application for AI integration. The following steps detail AI component integration into .NET.
Integration of AI into applications can be done by using appropriate frameworks and libraries. The following are the most implemented ones:
Azure Cognitive Services adds value through its capabilities in computer vision, speech recognition, and natural language processing (NLP).
If you’re hoping for easy wins, attaching your application to AI-enabled APIs might be the answer. A few examples are:
Yes, these AI-powered APIs do add value as they greatly simplify integration by allowing users to utilise AI capabilities without constructing models.
Hugging Face and Microsoft’s AI Builder are examples of platforms offering AI Builder-trained models, which saves time and resources that would have gone into training the model from scratch.
Although the pre-built AI-powered models are great, some users may have specific needs that force them to change or retrain the model to work better with certain app data. Luckily, ML.NET provides an accessible training framework with .NET.
AI integration may necessitate the refinement of your codebase to make room for additional AI features such as limbs. While integrating new components, keep your application modular and efficient. Software development service providers working with .NET would be beneficial in aiding these solutions.
With all the AI features in place, spend some days on comprehensive testing to ensure the new functionalities work as intended.
Imitate how users operate in the real world and ensure features do not degrade performance. Keep iterating your execution with more user feedback.
As motivation, look at these two examples wherein AI was added to .NET applications with great success.
A Financial Services company augmented its customer portal with an NLP chatbot through ML.NET. The chatbot halved the response time to customer queries, thereby significantly enhancing satisfaction and operational efficiency.
A Manufacturing Company integrated predictive capabilities into their legacy .NET application using Azure Cognitive Services. They could predict equipment failures with ancillary sensors, allowing them to avert millions of downtime.
Implementing AI, especially when integrating with legacy systems, can be challenging. Collaborating with a professional .NET Development Company, for instance, allows you to access unbounded guidance that helps you to:
With dedicated .NET development, one receives comprehensive guidance from concept through design and execution.
Integrating AI into legacy .NET software requires strong technical expertise with a clear understanding of your business goals & objectives. TechnoBrains Business Solutions excels in both, offering customized, AI-driven .NET software development services that enable organizations to stay ahead of the curve and deliver enhanced user experiences.
In an environment of ever-growing competition, integrating AI within .NET applications is no longer a valuable addition; it is imperative. With the appropriate methodologies and frameworks in place, you are able to convert your legacy systems into AI-powered engines.
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