doi: 10.18178/JAAI.2025.3.2.154-168
Automation-Multi-AI (AMAI): An Integrated Multi-AI Architecture for CPU-Based Analysis of Complex Structured Workflows
Prashant D. Sawant
- Founding Director, AI R&D, Ai-Discovery Company, Melbourne, Australia.
- Manuscript received March 16, 2025
- accepted April 30, 2025
- published June 17, 2025
Abstract
The present research introduces the Automation-Multi-AI (AMAI) architecture, a novel approach that integrates Alteryx analytics automation with Microsoft Copilot and Perplexity AI to process complex structured workflows. This integrated solution effectively handles sophisticated data analysis tasks without requiring specialized GPU infrastructure, providing a viable alternative to architectures like OpenAI, DeepSeek, Manus, and various Artificial Intelligent (AI) studios. Through empirical testing across multiple use cases, the article establish that AMAI delivers comparable or superior performance for structured analytical workflows while maintaining accessibility, reproducibility, and governance control. The architecture leverages Alteryx’s workflow automation capabilities, enhanced by AI-assisted guidance from Copilot and deep contextual research from Perplexity, creating a powerful synergy that addresses limitations in each individual system. AMAI’s CPU-centric design eliminates dependency on expensive GPU hardware, significantly reducing operational costs and making it particularly attractive for organizations seeking to balance analytical sophistication with operational constraints. The architecture provides a visual, codeoptional environment for data preparation, blending, and analysis, making it accessible to users without extensive technical expertise. Copilot’s conversational interface allows users to express analytical needs in natural language, further simplifying the workflow development process. Overall, the AMAI architecture represents a transformative step in AI-enhanced analytics by merging the capabilities of Alteryx, Microsoft Copilot, and Perplexity AI into a cohesive, CPU-driven ecosystem. It offers a cost-effective, easy-to-use, and highly efficient solution for diverse industries, addressing critical needs in enterprise analytics while circumventing the challenges of GPU dependency.
Keywords
- AMAI architecture
- Alteryx analytics automation
- Microsoft Copilot
- Perplexity Artificial Intelligent (AI)
- CPU-centric design
- structured analytical workflows
- DeepSeek
- Manus
How to Cite
Prashant D. Sawant, "Automation-Multi-AI (AMAI): An Integrated Multi-AI Architecture for CPU-Based Analysis of Complex Structured Workflows," Journal of Advances in Artificial Intelligence, vol. 3, no. 2, pp. 154-168, 2025. https://doi.org/ 10.18178/JAAI.2025.3.2.154-168
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).