Telecommunications
Kriterion transforms operational data, asset telemetry, site reports and fuel sensor data into foresight that telecom teams can act on.
11%
generator lifespan increase
4-9%
fuel saving across sample markets
56%
outage reduction from predictive maintenance
Kriterion pulls operational data across reporting, RMS, asset registry and ticketing sources to create a single centralised source of truth.
Aggregated data can then be enhanced with AI to surface insights such as fuel reconciliation, data quality, asset health and vendor performance.
Deep Digital Twins learn patterns in sensor and unstructured data from mechanical, electronic and electromechanical equipment.
These models can unroll forward predictions of sensor data, simulate asset futures and evaluate asset health at any point in time.
These use cases sit at the centre of the offering, turning operational data into maintenance, fuel, energy and network actions.
Predictive maintenance helps telecom operations reduce outages by scheduling interventions before equipment failures become service-impacting incidents.
AI predictions identify miscalibrated sensors and schedule recalibrations, improving the reliability of fuel reporting and consumption analytics.
Consumption trends, site-specific load profiles and sensor-detected tank levels support dynamic refuelling schedules, allocation planning and volume validation.
Route planning, delivery tracking and agentic logistics workflows create a real-time view of refuelling operations and truck utilisation.
Large towers increasingly resemble edge data centres, combining primary and backup power, HVAC, UPS, DC plant and site monitoring infrastructure.
AI-optimised parameters can reduce energy consumption while maintaining safe operating conditions across regional, access and edge infrastructure.
Outside-in IP prefix and ASN monitoring supports reachability checks, BGP-aware insights, site-level health views and SLA-grade reporting.
A telecom fleet deployment showed a 56% reduction in outages after predictive maintenance scheduling was introduced.
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Three sample markets showed normalised fuel savings of 9%, 8% and 4% through improved efficiency.
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A recalibrated site reduced reported consumption from 4.75 L/h to 4.05 L/h, contributing to a 3.57% fleet fuel improvement since recalibration.
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