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What are Generative AI Applications?
Generative AI-powered applications are solutions that leverage artificial intelligence (AI) to execute reward functions while performing certain tasks that usually require human intelligence. The executional program includes tasks that involve learning from the acquitted data, pattern recognition, decision-making, and natural language processing (NLP) and understanding. With generative AI services, Gen AI can be incorporated into core functionalities, applications, and they can be enhanced to adapt to contingent use cases, automate processes as well as personalize user experience. Computer Vision (CV), Machine Learning (ML) models, and NLP are integrated into the application’s architecture which allows extensive features such as predictive analytics, product recommendation systems, and personal chatbots.
Being a subset under the wide umbrella of AI, generative AI focuses on textual, image-based content, and code creation as per patterns continuously learned from the historical data. Through systematic adaptation, it has proven to be useful in accelerating the generative AI application development process through automatic code generation, content creation, and user interface designing. As per a Statista report, the generative AI market is expected to reach a size of approximately US $21.65 billion as of 2025 and is projected to rise at a CAGR of 37% to reach a market value of about US $143.51 billion by 2031.
Source: Grand View Research
Growing market size of applications of generative AI in the USA from 2020 to 2030
Steps of Building Generative AI Applications
Thorough and thoughtful strategizing forms the basis of use cases and robust technical stack while integrating pre-trained models in Gen AI applications. Given below is the step-by-step blueprint of building AI-powered applications and agentic AI solutions with the help of generative AI that can be useful for developers for navigating developmental complexities.
Step 1: This step involves defining AI use cases and user flows during the development process through identification of problems that the application is intended to solve, such as predictive analytics, smart searches, translation services, content generation, etc. This ensures alignment between expectations of the user from the enterprise app and Gen AI capabilities as the use cases and key performance metrics get defined and user flow is mapped out.
Step 2: This step involves development environment set up as the generative AI app use case, user flow, and concept is fixed. This is commonly done by using React.js for building responsive, swift, and smart front-end interfaces, and Node.js for the backend scalability and API integrations. This ensures seamless to and forth communication especially in case of generative AI for cybersecurity models and the user interface which forms the basis for delivering predictive results and real-time interactions through these apps.
Step 3: The next step after the technical stack gets ready is to integrate preferred generative AI models, such as OpenAI GPT-4 model for AI-based chatbots, proprietary enterprise models, or Hugging Face’s fine-tuned transformer models for content platforms. Generative AI business applications can feature generative capabilities, such as NLP or image generation by using SDKs and APIs.
Step 4: Furthermore, APIs and cloud infrastructure are connected in the pipeline as these applications require large datasets during training, real-time analytics, and model endpoints. Cloud providers such as GCP, AWS, and Azure can seamlessly host APIs, handle execution of model, and handle serverless architectures. Powerful cloud platforms and services also involve machine learning operations for tracking retraining requirements, performance of the model, and usage, such as in case of generative AI for retail.
Step 5: Next step requires re-training, testing and validation of the custom model and fine-tuning of the same for producing domain-specific results and intelligence. As per the use case, the model can be fine-tuned for delivering outcomes in AI-augmented software development with better accuracy, trained on proprietary data from the beginning, or validated for performance with respect to real-world scenarios. This is to ensure that the developed AI-based applications are intelligent, accurate, trustworthy, and functional in all aspects.
Step 6: The final step involves frontend optimization (UI/UX) for enhanced AI-powered interactions upon integration and validation using React.js. This helps in the generation of user-friendly and smarter inputs, real-time feedback loops, predictive visualizations, and dynamic content, therefore improving the application’s accessibility, performance, and usability dramatically.
Key Architectures in Generative AI Applications
Different types of Gen AI-powered apps require different computing power, training data, and architecture for smooth functioning of the trained model. Selection of the right architecture is especially important for building scalable apps, such as for agentic AI architecture, that deliver enhanced user experience and perform optimally. Given below are some effective architectures along with their advantages and disadvantages that are used worldwide for bringing these applications to life.
Serverless
This type of architecture allows ease of infrastructure management and generative AI inference. Cloud providers can automatically scale as per incoming demand, thus rendering it beneficial for cases of applications where the usage patterns are not predictable. Some well-known known Gen AI apps that function on such architecture include ChatGPT which is accessed through API as per user query-based demands, DALL-E mini that generates images as per user input, and virtual assistants that provide quick responses as per user request-based demands. Common technologies such as Step Functions, AWS Lambda, S3, and API Gateway are used for development and deployment of this architecture through AI development services.
Edge Computing
With this architecture, a generative AI app is placed closer to the user and functions in real-time due to reduced latency. Applications that require instant feedback or handling of privacy-focused data basically run on such architecture for improved response times. Some well-known applications running on edge computing architectures include AI-powered Augmented Reality (AR) filters in TikTok, Instagram, and Snapchat Lenses that alter backgrounds, objects, and faces in videos, and directly affect devices for real-time interactions, apps in autonomous vehicles and self-driving cars for real-time processing of data, safe navigation, and environmental response. Common technologies such as SageMaker Neo, PyTorch Mobile, TensorFlow Lite, and AWS IoT Greengrass are used for development and deployment of this architecture.
Microservices
This architecture is suitable for complex systems where each component can independently perform distinct tasks in the modular AI pipelines. In this architecture, the generative AI business applications are segregated into different parts providing independently deployable services. A few artificial intelligence examples in daily life include Spotify’s personal recommendation engine that conducts user behavior analysis for handling personalization, and content recommendations, AI-based content moderation in Quora, Facebook, and Instagram for detecting tags, offensive or plagiarized content, and providing product, service or business recommendations. Common technologies such as AWS ECS, SQS, DynamoDB, EKS, Kubernetes, and Docker are used for development and deployment of this architecture.
Batch Processing
This type of architecture is useful for any generative AI app that needs to handle large data volumes and bulk generative tasks although not in real-time. Applications that perform tasks requiring heavy data preprocessing and bulk content generation ideally utilize such architecture. Some well-known generative AI app that use such architecture include Lumen5 and Pictory.ai that automate creation of hundreds of videos in one go for marketing campaigns over social media, Jasper for automating creation of unique content for blog posts and bulk social media advertisements. Common technologies that are used for development and deployment of this architecture include EMR, SageMaker Processing, Amazon S3, and AWS Batch.
Hybrid Cloud
This architecture combines both on-premises and cloud-based resources. It is mostly used by enterprises that have regulatory needs and data privacy concerns. While scalable handling of inferences is done over the cloud, intensive model training is done on premises, in this case. Applications of generative AI such as the financial recommendation systems leverage the cloud for real-time high demand while keeping sensitive financial data on-premises, personalized healthcare diagnostic tools run recommendation and diagnosis algorithms over the cloud while keeping patient data secure on-premises.
Benefits & Challenges of Generative AI Applications
Benefits
Integration of generative AI into applications for enterprises and developers offers various advantages and has benefitted several industries, some of which have been mentioned below.
Efficient Operations
Routine tasks can be easily automated with the help of generative AI business applications. This reduces manual effort, increases savings in terms of costs, resource utilization, and productivity.
Lucid insights
Gen AI-powered applications can deliver data-driven, actionable, clear, and concise analytical insights for strategic and effective decision-making and overall growth in business.
Better Experience
medical These applications are enabled to produce constructive interactions and personalized content for enhanced user experience, engagement, satisfaction, and loyalty.
Swifter Development
Developers can expect higher efficiency in generative AI application development cycles with the help of automated code generation and assistance.
Features and benefits of generative AI applications for enterprises
Challenges
Although we discussed quite a few advantages of implementing Gen AI-powered apps, listed below are some of the challenges in building such apps.
Bias
Biased and inaccurate predictions by the generative AI systems can lead to a reduction in user trust and loyalty. Developers need to use ethical AI frameworks during development cycles and strategize conductance of thorough and continuous testing.
Complexity
Incorporation of generative AI application development into existing workflows and architectures can be very useful. Although such integrations may require resources with specialized skill sets and can be technically demanding. Development of such applications requires specific hands-on skillsets and AI experience that many teams may lack, such as automated front-end development, full-stack development, and AI deployment.
Training
Training generative AI models can be data intensive and require access to high-quality datasets. Developers need to leverage cloud-based architectures that render centralized and scalable data infrastructure to deal with AI model training involving large datasets.
Compliance
Several compliance standards and global regulations need to be cleared and adhered to by the developed application to be made publicly available such as GDPR, SOC2, and HIPAA.
Accelerate Your Generative AI App Journey with KritiKal
Artificial Intelligence is transforming the face of application development through enhancement tools and software that are intelligent and evolving. Applications of generative AI are enabling businesses to develop systems that are quick to learn and adapt continuously according to the dynamic environment. By partnering with KritiKal Solutions’ application development services, organizations can observe effective AI integration in their regular workflows and portfolio. Our expertise in front end services supports scalable and responsive architectures that can enhance the overall customer experience. We extend developmental services for all types of architectures including edge computing for faster processing, microservices for coordinated workflows, serverless for agile functions, batch processing for better efficiency, and hybrid cloud for resilience.
Choose our team of experts in custom generative AI model (multimodal large language model, VLM, and LMM) integration, private and on premises AI model deployment and optimization, cloud and API integration, agentic AI workflows, retrieval-augmented generation solutions, multi-modal data analysis, automated synthetic data generation, prompt engineering and optimization. Selection of the same alongside the right set of tools and technologies can ensure that the AI-powered application stays future-proof across the upcoming years and beyond. Please get in touch with us at sales@kritikalsolutions.com to know more about our solutions, services, and realize your generative AI requirements.
Harsh Khare currently works as a Senior Software Engineer at KritiKal Solutions. He has over 7 years of experience working with microservices monitoring (Prometheus, Grafana), automation of cloud-native infrastructures and CI/CD pipelines. He is proficiently skilled in AWS, Kubernetes, Jenkins, Docker, Terraform, and more. With his ability to support scalable deployments across environments, system reliability improvement, cost optimization skills, and collaborative working in teams, he has assisted KritiKal in delivering various projects to some major clients.