Is your Custom App AI Ready? Embracing the AI Revolution?

Custom Apps are the new standard. They’ve transformed how we interact, learn, and even run businesses. From simplifying tasks to offering personalized experiences, apps are here to stay.

But the future is even brighter. Artificial intelligence (AI) is taking industries by storm, and custom apps are leading the charge. If you are running your business with help of custom software (which almost 90% of all businesses are doing today), AI can improve customer service, transform healthcare and finance, and make your business run smoother with smarter decisions. 

Think of adding AI to your custom app like giving it a special boost! It can unlock features like personalized recommendations and predictions, making it a game-changer.

Hold on a sec! Before diving into AI, you need to make sure your app is prepped. 

An AI-ready custom app is like a well-oiled machine, ready to smoothly integrate AI. This means cleaning your data, fine-tuning your app’s structure, and figuring out how AI can specifically benefit your business. It also involves testing everything thoroughly to ensure the AI works flawlessly and delivers real value to your users. An AI-ready app gives you a competitive edge by providing accurate insights that keep your users happy.

Here’s how to get your Custom App AI-Ready :

1. Cleaning Data

Ensure data quality by removing errors, duplicates, and irrelevant information. This step is required for AI to function effectively, as it relies on high-quality data for accurate insights and predictions.

Develop data cleansing pipelines to identify and eliminate inconsistencies, duplicates, and missing values within the app’s data infrastructure. Utilize tools like Apache Spark and Open Refine for data preprocessing and cleansing.

2. Refine Custom App’s Structure

Optimize the architecture to seamlessly integrate AI features. This involves restructuring code for modularity and scalability, as well as optimizing performance to handle AI’s processing demands.

Modernize the codebase to improve modularity and scalability, utilizing version control systems like Git and static code analysis tools such as SonarQube. Document APIs using tools like Swagger for seamless integration of AI functionalities.

3. Clear the Way for AI

Prioritize tasks and allocate resources efficiently for AI integration. Collaboration among teams is vital to ensure timely completion of data preparation, model development, and testing phases.

Establish a clear project plan outlining tasks, timelines, and resource allocation for AI integration using project management software like Jira or Asana. Utilize team communication platforms such as Slack for effective collaboration among teams.

4. Defining specific AI Mission

Identify specific use cases where AI could add value to a custom app, considering user feedback and market research. Clear objectives and success metrics help in measuring the effectiveness of AI implementations.

Conduct user research and market analysis to identify impactful AI use cases, utilizing tools like SurveyMonkey for user surveys and Mixpanel for user behaviour analytics. Define clear goals and success metrics for each AI-powered feature.

5. Gather the Right Data

Collect relevant and high-quality data aligned with chosen defined AI use cases. This may involve implementing data collection mechanisms within the custom software and exploring external data sources for enrichment.

Develop a data acquisition strategy to gather high-quality, relevant data from internal and external sources, utilizing data collection SDKs and API integration tools. Implement data warehousing solutions like Amazon Redshift or Snowflake for efficient data storage.

6. Preparing Infrastructure

Upgrade custom app’s infrastructure, such as servers and databases, to support AI workloads effectively. Cloud migration and scalability considerations are important for handling AI processing demands.

Evaluate and upgrade the infrastructure to ensure it handles the processing demands of AI integration. Explore cloud migration options using cloud provider tools like AWS Management Console or Azure Portal.

7. Training AI model

Develop and train AI models using the collected data, experimenting with different algorithms and techniques to optimize performance. Monitoring and fine-tuning are important to ensure accurate and reliable AI functionalities.

Develop and train machine learning models using machine learning libraries such as TensorFlow or PyTorch. Utilize cloud-based machine learning platforms like Amazon SageMaker or Azure Machine Learning for model development and experimentation.

8. Integrate AI

Deploy the trained AI model into the custom software’s production environment, establishing communication channels through APIs. Thorough testing is required to validate the AI integration and ensure smooth operation.

Deploy the trained AI model into the app’s production environment, establishing communication channels through APIs and implementing model deployment frameworks like TensorFlow Serving or PyTorch Serving. Utilize monitoring and logging tools such as Prometheus or Grafana for real-time performance tracking.

By following these steps, you’ll get your custom app AI-ready and unlock its full potential. This will give you a competitive advantage and keep your users engaged with a smarter, more powerful custom app.

How does Himcos helps ?

Himcos can help! We specialize in getting custom apps AI-ready. Our services include data cleaning, application refactoring, and setting AI goals. Let’s future-proof your custom app together!