AI-Driven Plugin Architecture for Enterprise Integration

Authors

  • Sneh Lata Oakville, Ontario, Canada Author

DOI:

https://doi.org/10.32628/IJSRSET2512180

Keywords:

AI Integration, Plugin Architecture, Enterprise Automation, Machine Learning and NLP

Abstract

This journal outlines the strategic development, implementation, and optimization of AI-driven plugin systems for enterprise integration. Drawing upon real-world experience in managing end- to-end AI transformation initiatives, the document presents a modular, scalable approach to re - architecting legacy and modern enterprise software systems. It focuses on delivering automation, intelligence, and customizability through machine learning (ML) and natural language processing (NLP) technologies embedded into plugin frameworks.

📊 Article Downloads

References

Vaswani et al., “Attention Is All You Need”, 2017

Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, 2018

E2E MLOps Practices – Google Cloud AI/ML Guide, 2023

Hugging Face Model Documentation, 2024

Best Practices in Plugin-based Architecture, ThoughtWorks Tech Radar, 2023

Downloads

Published

26-06-2025

Issue

Vol. 12 No. 3 (2025): May-June

Section

Research Articles

License

Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sneh Lata, “AI-Driven Plugin Architecture for Enterprise Integration”, Int J Sci Res Sci Eng Technol, vol. 12, no. 3, pp. 1392–1394, Jun. 2025, doi: 10.32628/IJSRSET2512180.