AI Log Analyzer

Cut RCA time by 80%, slash escalations, and strengthen QoE with explainable AI 

See AI Log Analyzer in Action 

From Logs to Intelligence 

Modern networks generate massive volumes of logs across RRC, NAS, MAC, RLC, and PDCP layers. Manual analysis is slow, error-prone, and reactive - delaying anomaly detection, raising OPEX, and hurting QoE.  Amantya’s AI Log Analyzer ingests and correlates cross-layer logs, applies advanced ML models, and delivers explainable insights through intuitive dashboards. With modular, OSS/NMS-ready architecture, it transforms raw logs into actionable intelligence - driving faster fixes, lower costs, and stronger user experience. Designed with an explainability-first approach, it ensures every anomaly is not only detected but also understood, trusted, and acted upon. 

Real-World Outcomes 

80% Faster RCA 

80% Faster RCA 

Faster service restoration, fewer SLA penalties 

Real-Time Anomaly Alerts 

Real-Time Anomaly Alerts 

Faster detection; reduced downtime risk 

35% Fewer Escalations 

35% Fewer Escalations 

Lower support costs and faster MTTR 

50% Faster Regression  

50% Faster Regression  

Shorter release cycles, faster feature rollouts

Predictive Outage Alerts 

Predictive Outage Alerts 

Prevent outages before they impact customers 

Carrier-Grade Reliability

Carrier-Grade Reliability

Demonstrable SLAs; stronger customer trust

Lower OPEX, Higher QoE 

Lower OPEX, Higher QoE 

Long-term cost reduction and happier subscribers 

Automated Insights 

Automated Insights 

Data-driven ops and continuous performance gains 

Key Differentiators

Why Amantya’s AI Log Analyzer?

AI at the Core

AI at the Core

Every stage powered by ML/LLM models

Unmatched Speed

Unmatched Speed

From multi-gigabyte logs to insights in minutes

Explainability Built-In

Explainability Built-In

Transparent, trusted, non–black-box insights

Seamless Integration

Seamless Integration

Easy plug-in with bug tracking tools and operator OSS/NMS

Full-Stack Coverage

Full-Stack Coverage

RF, RAN, mobility, and QoE metrics

Proactive Detection

Proactive Detection

Flags failures before they impact QoE

Geo + Temporal Views

Geo + Temporal Views

Maps peak-hour congestion and location-specific issues 

Future-Proof Design

Future-Proof Design

Modular and ready for 5G, 6G, O-RAN, NTN 

Enterprise-Scale

Enterprise-Scale

Handles terabytes of logs across multi-network portfolio

Amantya’s AI Log Analyzer in Action

AI at Every Step 

AI-Powered RCA

AI-Powered RCA

Clusters defects and generates session traces in seconds - 80% faster

Cross-Layer Intelligence

Cross-Layer Intelligence

Correlates logs across L2/L3 to pinpoint failures

Predictive Insights

Predictive Insights

Detects anomalies and risk patterns before they impact users 

Explainable AI

Explainable AI

Provides root-cause context for every anomaly

Insightful Dashboards

Insightful Dashboards

Clusters defects and generates session traces in seconds - 80% faster

AI Toolkit

AI Toolkit

Isolation Forest, SVM, Clustering, Autoencoder, LSTM, Graph Causal Models

Flexible Integration

Flexible Integration

Easy plug-in for OSS/NMS and bug tracking systems

Scalable & Future-Proof

Scalable & Future-Proof

Linear scale-out for growing logs and evolving networks

Amantya’s AI Log Analyzer in Action 

How It Work AI at Every Step 

Step 1

Log Ingestion & Preprocessing

Collects raw NAS/RRC logs, KPIs, PCAPs, timestamps, and cell IDs.

Step 2

Preprocessing & Feature Engineering

Normalizes data, extracts latency, retransmissions, and message order.

Step 3

Clustering & Anomaly Detection

Groups normal vs. abnormal behavior using AI/ML (DBSCAN, K-Means, Isolation Forest).

Step 4

Sequence & Procedure Analysis

Detects slow attaches, failed PDU setups, or missing responses.

Step 5

Explainable Dashboards

Uses KPIs, heatmaps, anomaly clusters, and message traces to highlight where, when, and why anomalies occurred.

Specialized AI Modules 

Each core capability is powered by advanced AI models

AI-First Technolog. AI-Powered Workforce.
AI Anomaly Detection Engine - Instant Alerts

Pinpoints abnormal behavior in seconds

  • KPI Outliers: Detects accessibility, throughput, and retainability issues.
  • Silent Degradations: Flags problems before users notice, preventing downtime.
  • Faster RCA: Accelerates root-cause analysis with AI-driven traces.

Uncovers patterns hidden deep in raw logs

  • Pattern-Based Alerts: Surfaces recurring issues for proactive remediation.
  • Clustered Logs: Groups logs into meaningful patterns using DBSCAN/K-Means.
  • Deviation Detection: Autoencoders highlight anomalies from normal sequences.

Understands how one failure triggers another

  • Sequential Capture: LSTMs track sequential log behavior across sessions.
  • Cross-Layer Links: Graph-based causal inference connects failures across layers.
  • Predictive Failures: Identifies cascading issues before they escalate

Translates logs into clear, actionable insights

  • NLP Tagging: Tags cells, procedures, and anomalies for easy understanding.
  • Trend Summaries: Generates summaries and trend insights operators can act on.
  • Decision Support: Highlights actionable insights to guide engineers and testers.

Don’t just see anomalies - understand them

  • Why It Happened: Each anomaly is paired with its root cause - from retransmissions and missing responses to RF degradations.
  • Drill-Down Clarity: Inspect exact log sequences or KPI deviations that triggered the alert.
  • Confidence: Transparent AI insights engineers and operators can act on immediately.
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