Predictive AIOps
and recommends fixes before they reach your customers
What Is Predictive AIOps?
Too many operators still learn about network problems from their customers. The cost shows up in outages, repeat tickets, and operational overhead. Predictive AIOps shifts operations from reactive to proactive. AI continuously learns normal network behavior, flags anomalies as they emerge, and identifies the root causes across the RAN, the core, and services. The result: faster resolution, lower operating costs, and outages prevented before subscribers are affected.
100%
of user-plane traffic analyzed per subscriber, in real time, with no sampling
30%
lower mean time to repair for faults and complaints after deploying AI agents.
75%
of complaints pre-empted.
Seconds, not hours
to tell whether an issue sits in the RAN, the core, or a service (RADCOM).
How Can RADCOM Help You?
RADCOM ACE turns a flood of network data into a short list of priorities, then takes teams from alert to root cause on a single platform. RAN Recommendations and RADCOM NWDAF turn those insights into optimization and predictive analytics across the RAN and the 5G core, and RADCOM Neura AI agents recommend the right fix for each issue, supporting a move to more automated operations.
Earlier detection
anomalies flagged automatically, without static thresholds.
Fewer outages
rising failures caught before they spread.
Faster resolution
from alert to root cause on one platform.
Precise location
issues pinpointed to a building, street, or sector edge.
Automated optimization
recommendations fed into SON for self-optimizing networks .
AI agents that act
coverage issues identified, with recommended fixes.
Trusted by Tier-1 networks across North America, Europe, and Asia-Pacific
Release Cause Distribution (RCD)
RCD continuously monitors every failed network event, learns normal failure patterns, and flags early spikes in failed call setups or dropped sessions, identifying affected subscribers before the issue spreads.
Detect
Shows where and when failures are rising, and who is affected.
Investigate
Every alert provides direct access to the correlated end-to-end sessions that triggered it.
Confirm
Packet-level evidence confirms the root cause and supports fast handoff to equipment or roaming partners, all within one platform.
Anomaly Detection and
Root-Cause Analysis
Critical issues rarely announce themselves. RADCOM ACE uses AI to turn billions of daily network events into actionable intelligence: detecting anomalies early, pinpointing what matters, and accelerating resolution to the root cause. 95% less time to manage major network events, from hours to about minutes, using AI agents
Dynamic Baselining
Automatic baselines across network and subscriber KPIs, so teams act on real anomalies instead of monitoring dashboards.
Dynamic Baselining
One dataset takes teams from a dashboard KPI to the underlying sessions and packets, without switching tools.
RAN-to-Core Correlation
Identifies in seconds whether an issue sits in the radio network, the core, or a specific service.
Geospatial Detectors
Pinpoints issues to a single building, street, or sector edge that cell-level averages would hide, with AI agents recommending the fix for that location.
RAN Recommendations
Optimize the RAN on what subscribers actually experience
Traditional optimization relies on cell-level KPIs that hide what subscribers actually experience, especially on the move. RADCOM measures real subscriber experience from live sessions, not cell averages, and turns it into evidence-based recommendations.
RAN Analytics
Voice and data quality correlated with radio conditions, with recommendations available from executive dashboards to engineering tools via API.
From Virtual Drive Test to SON
A phased path from virtual drive testing to deeper analytics to automated recommendations fed into Self-Organizing Networks (SON), the network’s self-optimization layer.
Automated Fix Recommendations
Common RAN issues, including cell configuration conflicts, overshooting cells, and inactive “sleepy” cells, are detected, and a targeted recommendation is provided for each.
Measured Results
Targeted changes, such as reducing interference, cleaning up neighbor-cell relations, and tuning mobility settings, deliver clear before-and-after gains in call quality.
5G Fixed Wireless Access
The same approach balances 5G Fixed Wireless Access and mobile traffic, improving experience for both.
About 40% fewer drive tests
with Virtual Drive Test.
AIOps Agents
The RAN Coverage Analyzer, part of RADCOM Neura AI Agent Suite, identifies and groups coverage issues by region and recommends targeted fixes, all grounded in live network traffic. RADCOM’s 10-Second Interval solution removes the blind spot. It measures voice quality in 10-second increments across the full RTP session and ties each interval to the radio conditions and serving cell at that exact moment. The result is a RAN-to-core timeline of the customer’s voice experience, so operators know when quality dropped and which cell caused it.
65%
of operators see agentic AI as transformational or high value
54%
prioritize autonomous fault resolution
(GSMA Intelligence, produced with RADCOM).
40%
fewer drive tests and field visits with RAN Coverage Analyzer
Further Reading
FAQs
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Predictive AIOps uses AI to shift network operations from reactive to proactive. It continuously learns normal network behavior, flags emerging anomalies, and identifies root causes across the RAN, the core, and services, so operators can prevent outages before subscribers are affected.
-
RADCOM ACE builds dynamic baselines automatically across network and subscriber KPIs. AI turns billions of daily network events into a short list of real anomalies, so teams act on what matters instead of watching dashboards.
-
RCD monitors every failed network event, learns normal failure patterns, and flags early spikes in failed call setups or dropped sessions. Each alert links to the correlated end-to-end sessions, and packet-level evidence confirms the root cause, so issues are caught before they spread.
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RAN-to-core correlation shows in seconds whether an issue sits in the radio network, the core, or a specific service. Using AI agents, operators spend 95% less time managing major network events, cutting the work from hours to minutes.
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Yes. Geospatial Detectors pinpoint issues to a single building, street, or sector edge that cell-level averages would hide, and AI agents recommend the fix for that location.
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RADCOM measures real subscriber experience from live sessions and detects common RAN issues such as cell configuration conflicts, overshooting cells, and inactive "sleepy" cells. It provides a targeted recommendation for each, on a phased path from virtual drive testing to automated recommendations fed into SON.
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The RAN Coverage Analyzer, part of the RADCOM Neura AI Agent Suite, groups low-coverage, no-coverage, and interference issues by region and recommends targeted fixes, all grounded in live network traffic. Operators run about 40% fewer drive tests and field visits.
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RADCOM analyzes 100% of user-plane traffic per subscriber in real time, with no sampling. After deploying AI agents, operators see 30% lower mean time to repair for faults and complaints, and 75% of complaints pre-empted.