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AI-Powered Telecom Mast Inspection and Digital Twin Platform for Scalable Infrastructure Management
  1. case
  2. AI-Powered Telecom Mast Inspection and Digital Twin Platform for Scalable Infrastructure Management

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AI-Powered Telecom Mast Inspection and Digital Twin Platform for Scalable Infrastructure Management

sparkbit.pl
Telecommunications
Information technology

Challenges in Scalable Telecom Infrastructure Management

Telecom providers face significant operational inefficiencies due to manual mast inspection processes requiring extensive human labor, inconsistent system architecture causing development delays, and inability to scale effectively for new market expansion. Current workflows involve time-consuming manual data annotation and processing, with over 129,000 masts in the US alone requiring digitalization.

About the Client

A US-based startup specializing in AI-driven telecom mast inspection and digital twin solutions for infrastructure management

Key Project Goals

  • Automate mast inspection processes using ML-driven digital twin analysis
  • Reduce processing time for new masts by 60% through AI implementation
  • Implement scalable microservices architecture for European market expansion
  • Standardize data models and development workflows across multiple teams
  • Enable automated discrepancy detection between designed and actual mast states

Core System Capabilities

  • 3D object recognition and tagging using deep learning
  • Automated photogrammetry data processing pipelines
  • Digital twin visualization dashboard with asset inventory
  • ML-driven space utilization analysis for equipment planning
  • Real-time model training and deployment pipelines (MLOps)
  • Automated comparison tool for mast state discrepancies

Technology Stack Requirements

Machine Learning (ML)
Computer Vision
MLOps (ML Operations)
AWS Cloud Infrastructure
GitLab CI/CD
3D Graph Analysis
Microservices Architecture

System Integration Needs

  • Drone photogrammetry systems
  • Existing telecom asset databases
  • Geospatial reference systems
  • DevOps monitoring tools

Operational Requirements

  • Horizontal scalability for 100k+ mast capacity
  • 99.9% system uptime SLA
  • Real-time processing pipeline monitoring
  • Role-based access control (RBAC)
  • Automated model versioning and rollback

Expected Business Outcomes

Implementation will reduce mast processing time by 60%, enabling rapid market expansion while maintaining system stability. Automated ML pipelines will cut deployment cycles from weeks to hours, and the modernized architecture will support seamless integration of future AI capabilities. The solution will position 5x5 Technologies to dominate both US and European telecom infrastructure markets through unmatched operational efficiency.

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