top of page
  • Writer's pictureSuris Code Team

How AI is Transforming Software Development: Tools and Practical Uses


Artificial intelligence (AI) is revolutionizing software development, changing how developers tackle complex problems and enhance efficiency. In our software factory, we have witnessed the significant impact of these innovations.


AI tools for developers

There are various AI-powered tools designed to assist developers. One of the most well-known is GitHub Copilot. This tool facilitates code autocompletion based on context and helps understand existing code, especially when working with legacy code. Additionally, Copilot can suggest complete code snippets, accelerating developers' workflow.


3 AI use cases in problem-solving solutions

1. OCR and ICR

AI is applied in multiple solutions for pre-existing problems, significantly improving outcomes. A notable example is OCR (Optical Character Recognition) services, which convert printed text in physical documents into digital text, streamlining processes that previously required manual data entry.

Traditional OCR has limitations with handwritten text or non-standard fonts. This is where ICR (Intelligent Character Recognition) comes into play, designed to recognize handwritten text. Nowadays, there are cloud services that combine OCR algorithms with AI, capable of interpreting handwritten text and querying content in printed documents.


Notable OCR and ICR services
  • Azure AI with Form Recognizer: Ideal for processing forms and structured documents.

  • Google Cloud with Document AI: Offers high accuracy in text recognition and document classification.

  • AWS with Amazon Textract: Specialized in extracting text, data, and tables from scanned documents.


2. Text generation with AI

We also have APIs for text generation using AI, such as ChatGPT or Claude AI. Although they do not use OCR technology, they can accurately interpret text in images, adding value to document processing and analysis. These tools can generate descriptions, summaries, and content based on input data, improving productivity and work quality.


3. Text interpretation and sentiment analysis

Another relevant application of AI is its ability to interpret texts, understanding the intent behind them. For example, AI can analyze user messages to determine their emotional tone, categorize and tag information, speeding up processes that would otherwise require human intervention. This is especially useful in customer service, digital marketing, and online community management.


Conclusion

AI continues to open new possibilities in application development, allowing us to offer value-added products and services, speed up processes, and solve problems more efficiently. Integrating AI into our projects not only enhances efficiency but also opens up new opportunities for innovation.


How have you used AI in your developments?

We would love to hear how you have integrated AI into your projects. Share your experience with us in the comments or contact our team to explore how we can collaborate and take your ideas to the next level.



4 views0 comments

Comments


bottom of page