AI Solution Architecture Prediction System
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Solution Overview
Problem
Current systems for determining IT service architecture are cumbersome, prone to errors, and non-scalable, often relying on historical data and failing to consider new tools and techniques, as well as varying industrial sectors and client requirements, making them inefficient and inflexible.
Innovation Solution
A solution architecture prediction system utilizing AI-based tools and techniques to analyze market, historical, and infrastructure data, identifying optimal tools and platforms through a hybrid AI model that learns from expert inputs and evaluates credibility scores for both previous and potential solutions, including new technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If available systems use historical data only, then implementation constraints are respected, but new tools and techniques are not considered
Solution Approach 1:
The system performs preliminary actions by collecting and storing both historical data and current market data about tools and techniques before architecture determination. This allows the system to consider new tools while maintaining reliability through established historical information, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The system introduces an intermediary AI-based architecture determination system that mediates between historical data and new tools/techniques. This intermediary evaluates both sources and integrates them to determine optimal architecture, allowing consideration of new tools while maintaining reliability through systematic evaluation.
2Productivity
If manual architecture determination is used, then complex analysis can be performed, but the process is cumbersome and non-scalable
Solution Approach 1:
The system replaces the mechanical manual analysis process with an AI-based automated system. The AI model performs complex architecture determination analysis automatically, achieving scalability while maintaining the capability for complex analysis through machine learning algorithms rather than human manual processes.
Solution Approach 2:
The system enables self-service by allowing the AI model to autonomously determine architecture without requiring extensive manual intervention. The system automatically collects data, analyzes requirements, and generates architecture recommendations, improving productivity while managing complexity through automated workflows.
3Adaptability or versatility
If a fixed architecture determination process is used, then consistency is maintained, but flexibility to accommodate different sectors and requirements is lost
Solution Approach 1:
The system implements dynamics by making the architecture determination process adaptive rather than fixed. The AI model dynamically adjusts its analysis based on the specific industrial sector, client requirements, and available tools, maintaining consistency through standardized evaluation criteria while achieving flexibility through context-aware recommendations.
Solution Approach 2:
The system applies local quality by tailoring the architecture determination to specific local contexts such as different industrial sectors and client requirements. While maintaining a consistent overall process framework, the system adapts its analysis and recommendations to local needs, resolving the contradiction between adaptability and consistency.
Data Source
AI summary
A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.