The Problem
Tillman Infrastructure, a large tower infrastructure operator, needed an advanced solution to digitally map, analyse, and optimise its telecom tower portfolio. Key challenges included:
- Unstructured data from drone images, LiDAR scans, sensor readings, and weather feeds could not be processed into actionable insights at scale
- Manual inspections were time-consuming and lacked real-time accuracy
- Limited visibility into tower conditions, tenancy load, and structural integrity made proactive maintenance difficult
To enhance tower lifecycle management, increase tenancy revenue, and streamline maintenance, Tillman required an automated, AI-ready data approach.
The Solution
The AI Data Preprocessor was deployed to convert unstructured tower data into a centralised, AI-ready pipeline. The AnySource Data Combiner extracted and structured data from drone imagery, LiDAR scans, and field sensors, while the Data Context Builder enriched it with environmental context, load correlations, and structural health computations. This preprocessed output fed into NextqoreAI Analytics — enabling predictive maintenance and tenancy optimisation across the tower portfolio.
- Automated Image Processing — Key metrics extracted from digital twin images enabled predictive maintenance, tenancy optimisation, and report generation such as TLVA (Tower Loading Validation Analysis)
- Unstructured Data Tagging — Image-based data converted into structured formats with integrated metadata covering tenancy details, tower conditions, and equipment load
- Weather & Environmental Data Correlation — Wind speed, temperature, and weather data combined with tower sensor readings to assess performance and structural risk
- Predictive Maintenance Insights — AI-ready data enabled identification of structural weaknesses, corrosion risks, and load-bearing capacity thresholds before failures occurred
- Revenue Opportunity Mapping — Preprocessed tenancy and capacity data surfaced available slots to help maximise tower utilisation
AnySource Data Combiner
Drone imagery, LiDAR scan outputs and real-time sensor data from field devices were ingested as structured sources. Wind
speed, weather conditions and tenancy records from IT systems were combined into a single validated pipeline —
converting previously unstructured and siloed tower data into a unified, auditable dataset ready for contextualisation.
Data Context Builder
The combined dataset was enriched with:
- Conditions — weather and environmental signals correlated to structural load and performance readings
- Correlation — tenancy occupancy data cross-referenced with tower load conditions and maintenance history to identify risk patterns
- Computation — derived structural health indices, load capacity metrics, and maintenance priority scores calculated per tower
- OBS — Ontology Based Semantics defining relationships between tower assets, tenants, structural components, and environmental factors — enabling AI models to reason across the full tower lifecycle
The Result
- Automated Tower Mapping — digital twins deliver a real-time, high-fidelity view of each tower across the portfolio
- Efficient Maintenance Planning — AI-preprocessed structural data reduced manual inspection dependency and enabled predictive maintenance scheduling
- Optimised Tenancy Management — enhanced data visibility across tower capacity maximised rental slot identification for revenue growth
- Improved Structural Health Monitoring — continuous tracking of load conditions and environmental factors prevents outages before they occur
- Seamless Integration — data extracted from the digital twin, preprocessed by the AI Data Preprocessor, and delivered to NextqoreAI Analytics — enabling deeper operational intelligence and lifecycle efficiencies
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