Telecommunications

TelCo giants invest billions of dollars annually in their networks, infrastructure and the software systems that manage, monitor and delivery services. For the last few decades, telcos have made use of point solution specialists for various systems - OSS/BSS, CPQ and others, and often these systems do not integrate well, or there is significant overlap.

We aid the modern telco with governance, data science and ensuring these vital services remain efficient, unified and safe.

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Challenges we solve

Network Anomaly Detection Root Cause Analysis Autonomous Remediation Revenue Operations

Network Anomaly Detection

Telecom operators manage vast, complex networks where downtime or performance degradation directly impacts customer experience and revenue. Traditional monitoring systems rely on static rules that struggle to keep pace with evolving traffic patterns and emerging threats.
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With AI-driven analytics, operators can detect anomalies in real time across call data records, traffic flows, and infrastructure logs. Machine learning models continuously adapt to changing baselines, identifying subtle signals of fraud, congestion, or service disruption before they escalate. For example, a spike in call drops across a region can be flagged instantly, triggering early investigation.

This proactive detection reduces outages, minimizes fraud, and helps operators maintain the always-on service customers expect.

Root Cause Analysis

When issues occur, identifying the true source—whether in network hardware, configuration, or external dependencies—can be time-consuming and costly. Traditional methods often involve manual log reviews across siloed systems.
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With Pygio Assembly, our AI Acceleration framework built for Snowflake, operators gain a unified platform for root cause analysis. Assembly’s unstructured data crawlers ingest logs, tickets, and alerts; its semantic layer maps them into meaningful business and technical objects. AI models can then correlate symptoms across domains, surfacing the likely root cause quickly and reliably.

Instead of hours of investigation, engineers can zero in on the faulty router, misconfigured policy, or external peering issue within minutes—dramatically reducing mean time to repair (MTTR) and ensuring network reliability.

Autonomous Remediation

Beyond detection and diagnosis, the next frontier is autonomous remediation—networks that can heal themselves. Many telecoms still rely on manual playbooks and operator intervention, which slows resolution and increases costs.
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By embedding AI agents trained on historical incidents and validated remediation steps, operators can automate responses to common failures. For instance, when a bandwidth bottleneck is detected, traffic can be automatically rerouted; if a misconfiguration is identified, the system can roll back to the last stable state.
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This closes the loop from monitoring to action, reducing downtime and operational overhead. Over time, autonomous remediation creates more resilient, self-optimizing networks that scale without proportionally increasing human resources.

Revenue Operations

Revenue assurance remains a top challenge in telecommunications, with leakage from billing errors, fraud, or process inefficiencies costing billions annually. Legacy systems make it difficult to reconcile data across billing, CRM, and network platforms.

Pygio Assembly provides the accelerators to unify and govern this data, cleansing and enriching it for consistency across the revenue chain. By applying AI-driven anomaly detection on top, operators can identify discrepancies in billing events, detect fraudulent SIM activity, or flag misapplied tariffs in near real-time. The business-focused semantic layer ensures that revenue events are mapped directly to customer accounts, contracts, and network usage, reducing reconciliation complexity.

This approach transforms revenue operations from reactive error correction into a proactive, automated assurance capability—protecting margins while enhancing customer trust.