AI-Driven Automation in Telecom Infrastructure: A Cloud-Native Approach

This paper presents a novel, patent-pending automation platform that leverages artificial intelligence and a cloud native architecture to transform the generation of Radio Frequency Data Sheets (RFDS) and telecom infrastructure diagrams [4]. These documents are critical for network deployment, vendor compliance, and regulatory approvals, particularly for standards such as E911. Traditionally, this process has been manual, error prone, and time-intensive. The proposed platform automates the entire workflow — from raw configuration ingestion to validated diagram rendering — using a layered system of microservices, AI inference engines, and compliance validation modules.Key technical innovations include schema inference models that normalize diverse vendor inputs, a reinforcement learning agent that optimizes RF jumper layouts, and anomaly detection systems for early error correction. Deployed in production environments supporting Tier-1 telecom operations, the platform has achieved measurable improvements in documentation accuracy, turnaround time, and quality assurance metrics. The work represents a significant advancement in telecom automation and serves as a reference implementation for scalable, intelligent infrastructure design. The author’s technical leadership in architecture and deployment further underscores the originality and impact of this contribution within the field.