Are your network operations teams reactive -- finding out about performance degradation from customer complaints rather than from network monitoring?
Is capacity planning still based on historical averages rather than demand forecasting that accounts for geographic and temporal variability?
When a network incident occurs, how long does it take to identify the root cause -- and how much of that time is engineers manually correlating data across multiple monitoring systems?
AI for Telecom Network Optimisation
Network performance AI that predicts faults before customers experience service degradation, identifies underutilised and overloaded network segments, and surfaces root cause faster when incidents occur.
For network operations teams managing growing traffic volumes with finite spectrum and infrastructure investment.
Predictive fault detection from network telemetry that surfaces degradation signals before service impact occurs
Capacity demand forecasting by cell, region, and time window for proactive capacity management
Automated root cause analysis that correlates fault signals across network layers to identify cause faster
QoS monitoring with anomaly detection and impact-severity classification for triage prioritisation
RaftLabs builds AI network optimisation systems for telecom operators including predictive fault detection from network telemetry data, capacity planning and demand forecasting, traffic routing optimisation, Quality of Service (QoS) monitoring and anomaly detection, network performance dashboards, and root cause analysis automation for network incidents. AI network optimisation reduces unplanned service degradation events, improves network utilisation efficiency, and reduces mean time to resolution for network faults. Projects are scoped at a fixed cost after an assessment of your network infrastructure, monitoring data sources, and specific optimisation objectives.
100+Products shipped
·24+Industries served
·FixedCost delivery
·12-16Week delivery cycles
Network operations that are still primarily reactive have a cost they do not always quantify
An unplanned outage in a high-traffic cell causes customer experience degradation, service credits, and potential churn. Network Operations Centres that discover service quality issues from customer complaints or helpdesk ticket volumes are discovering them after the damage has started. The monitoring data that would have predicted the fault was generated -- it just was not being watched for the right signals.
AI network optimisation puts predictive intelligence on top of the monitoring data your network infrastructure already generates. Fault prediction, capacity forecasting, and root cause acceleration from the telemetry and performance data your NOC already collects.
What we build
Predictive fault detection
Machine learning models trained on your network telemetry data and historical fault records to predict equipment failures and service degradation before they cause customer impact. Signal sources: SNMP traps, interface counters, optical power levels, cell KPIs, call quality metrics, and vendor-specific telemetry. Anomaly detection on the combination of signals that preceded past faults, not just individual threshold breaches. Prediction output: probability of fault within a defined time window (1 hour, 4 hours, 24 hours) with the contributing signals surfaced for NOC engineer review. Early warning that shifts the response from reactive to proactive.
Capacity planning and demand forecasting
Demand forecasting by network segment, geographic cell, and time period for proactive capacity management. Traffic growth trend analysis distinguishing sustainable organic growth from seasonal peaks and event-driven spikes. Cell-level capacity utilisation monitoring with congestion risk scoring. Capacity headroom reports that identify segments approaching utilisation limits before congestion impacts service quality. Demand-driven capital expenditure prioritisation: which cells need capacity upgrades first based on projected utilisation growth, not just current utilisation. For operators managing spectrum and infrastructure investment decisions, the data layer for evidence-based planning.
Automated root cause analysis
Root cause acceleration for network incidents by correlating fault signals across network layers, equipment types, and geographic regions automatically. When a performance incident is detected, the system queries historical fault patterns, correlated upstream and downstream signal changes, and equipment maintenance history to surface the most likely root cause candidates. Root cause hypotheses presented with supporting evidence and confidence scores rather than requiring engineers to manually correlate data from five different monitoring tools. Mean time to diagnosis reduced by surfacing relevant context in minutes rather than requiring manual data assembly.
QoS monitoring and anomaly detection
Quality of Service monitoring across your key performance indicators: call success rate, data throughput, latency, packet loss, voice MOS scores, video buffering ratios. Statistical baseline modelling that distinguishes normal QoS variation from genuine service degradation. Anomaly detection sensitive enough to catch degradation before it crosses thresholds that trigger alarms, but specific enough to avoid alert fatigue. Impact severity classification: anomalies assessed by affected customer count, revenue impact, and SLA exposure to determine triage priority. The QoS intelligence that gives your NOC team an accurate picture of service quality across the network without manually checking every KPI dashboard.
Network performance dashboards
Operational dashboards for NOC teams, engineering, and management. Real-time network health overview: active incidents, sites with degraded performance, pending maintenance events. Cell performance heat maps showing throughput, utilisation, and QoS by geographic area. Historical trend views for performance review and SLA reporting. Incident timeline view for post-incident analysis. Executive reporting: availability, customer-impacting event counts, and SLA compliance over reporting period. Custom views for different stakeholder roles -- NOC operators need different data than engineering managers and network planning teams. Built on your existing data infrastructure.
Incident management integration
Integration with your existing incident management and ticketing systems (ServiceNow, Jira, BMC Remedy, or custom). Automatic incident creation when AI detection triggers above a defined severity threshold, with the relevant performance data and root cause hypotheses pre-populated in the ticket. Incident correlation to prevent duplicate tickets for the same underlying fault appearing from multiple monitoring sources. Escalation routing based on fault type, affected customer count, and time-of-day. Post-incident analytics: time to detect, time to diagnose, time to resolve, and trend of these metrics over rolling period. The connection between AI detection and the human response workflow that acts on it.
Frequently asked questions
Network optimisation AI works with the telemetry your network infrastructure already generates. For mobile networks: eNodeB and gNodeB KPIs (throughput, PRB utilisation, call setup success rate, handover success rate), alarm logs, and drive test data. For fixed and IP networks: interface utilisation and error counters from SNMP or streaming telemetry (gRPC/OpenConfig), BGP route change events, and optical performance monitoring data. For quality metrics: call quality data from probes or user-reported metrics, and service delivery platform performance data. Most mature networks generate sufficient telemetry for predictive analytics. The data assessment phase determines what is available, at what granularity, and what additional instrumentation would improve prediction accuracy.
We integrate with existing NMS, OSS, and monitoring platforms rather than replacing them. Data collection from your existing systems via API, database connection, or telemetry stream. Common integrations: Nokia NetAct, Ericsson Network Manager, Huawei U2000/iMaster NCE, Zabbix, Prometheus, and vendor-specific EMS platforms. For networks with limited centralised telemetry collection, we design a telemetry aggregation layer that pulls from distributed sources into a time-series database suitable for AI processing. Integration scope is mapped during the discovery phase.
A focused implementation covering predictive fault detection and QoS anomaly monitoring for a defined network scope (single technology layer or geographic region) typically delivers in 12-16 weeks. Broader implementations adding capacity forecasting, root cause analysis, and dashboard delivery for a full multi-technology network run 16-24 weeks. Timeline depends on telemetry data quality, integration complexity, and number of network elements. We deliver in phases so you have working fault prediction operational before the full scope is complete.
Yes. AI network optimisation is designed to augment your NOC team, not replace your existing tools. Your existing NMS continues to handle alarm management and configuration. The AI layer adds predictive intelligence, anomaly detection, and root cause acceleration on top of it. NOC engineers use their existing workflows and tools, with AI-generated alerts and root cause information surfaced as additional context in the incident ticket or a NOC dashboard. Integration with your existing incident management workflow ensures that AI detections enter the same handling process as other alerts. We work within your current NOC operating model.
Talk to us about your network optimisation project.
Tell us your network type, monitoring infrastructure, and the specific performance problem you are trying to solve. We will scope the AI system and give you a fixed cost.
