Codeless AI Workflow System for Rapid Deployment
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Solution Overview
Problem
Current AI solutions face challenges in speed of deployment, efficiency, and scalability, leading to prolonged development cycles and redundant development of AI components, with a lack of centralized repositories for reusable components, resulting in inefficiencies and low-quality developments.
Innovation Solution
A system and method for codeless creation of AI workflows using AI and Generative AI, which receives user requests, processes data, identifies AI service nodes, generates workflows, and deploys them onto external systems, enabling rapid development and reusability of AI components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional AI solution development methods are used, then comprehensive AI functionality can be achieved, but development time becomes excessively long (six months to a year or more)
Solution Approach 1:
The system performs preliminary actions by pre-defining AI service nodes, connectors, and actions in a reusable library before actual workflow creation. These pre-configured components can be directly selected and assembled during workflow development, eliminating the need to create them from scratch each time, thus dramatically reducing development time while maintaining functionality
Solution Approach 2:
The system creates and maintains a library of reusable AI service nodes, connectors, and actions that can be copied and reused across multiple workflows. Instead of recreating the same AI components for each new workflow, developers can copy existing validated components from the library, ensuring consistency and reducing development time
2Adaptability or versatility
If AI components are recreated for each project, then customization can be achieved, but redundant development efforts increase and efficiency decreases
Solution Approach 1:
The system creates universal AI service nodes, connectors, and actions that can serve multiple different workflows and use cases. These reusable components are designed with configurable parameters that allow them to adapt to different projects while maintaining their core functionality, enabling both customization and efficiency
Solution Approach 2:
The system performs preliminary configuration of AI components with common settings and parameters that can be reused across multiple projects. By pre-configuring these components with adaptable settings, the system enables quick customization for different use cases without requiring complete recreation, thus improving both versatility and productivity
3Reliability
If manual customization is performed for each AI deployment, then specific requirements can be met, but flexibility decreases and decisions become hardcoded
Solution Approach 1:
The system implements dynamic workflows where decisions and actions are not hardcoded but can be modified through the visual editor at runtime. The workflow engine dynamically evaluates conditions and executes appropriate actions based on real-time data, allowing the same workflow to adapt to different scenarios without requiring manual recoding, thus maintaining both specificity and flexibility
4Reliability
If comprehensive validation and metadata generation are performed at each stage, then quality and reliability improve, but processing time increases
Solution Approach 1:
The system performs validation and metadata generation preliminarily during workflow design and component selection phases rather than waiting until complete deployment. By validating components and generating metadata incrementally as the workflow is being built, the system ensures quality without adding significant overhead to the final deployment process
Data Source
AI summary
Artificial intelligence (AI)-based systems and methods for AI application development using codeless creation of AI workflows is disclosed. The system receives request for creating an artificial intelligence (AI)-based workflow from the user device. Further, the system obtains input data from data sources and pre-process the obtained data using AI based pre-processing model. Further, the system identifies plurality of AI and Generative AI service nodes to be executed on the pre-processed data. The system further generates an AI-based workflow by connecting AI and Generative AI service nodes. Further, the system generates a metadata for AI and Generative AI service nodes by executing each of the identified plurality of AI and Generative AI service nodes. The system validates the metadata based on AI-based rules. Furthermore, the system determines actions to be performed on the metadata based on results of validation and performs the set of actions on the AI-based workflow. Additionally, the system deploys the AI-based workflow onto external system based on configuration parameters.