AI Solutions for the Telecommunications Industry
ThirdEye Data works with telecom organizations as a domain-aware AI engineering partner. We build applied AI systems that strengthen network intelligence, automate operational workflows, and support faster, more reliable decisions across telecom operations.
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What We Do for Telecommunications: Business Value We Bring In
We help telecom teams move from reactive operations to predictive and intelligence-driven execution.
Our work focuses on operational areas where delays, noise, and manual coordination create risk. This includes network monitoring, quality assurance, ticket handling, traffic planning, and preventive maintenance.
By combining AI engineers with hands-on understanding of telecom KPIs, NOC workflows, ticket lifecycles, and network performance metrics, we design solutions that fit seamlessly into existing OSS, BSS, and operational environments.
The outcome is improved network reliability, faster issue resolution, lower operational overhead, and more predictable service quality.
Our Valuable Customers Who Trusted Us
Telecommunications AI Solutions We Deliver
Agentic AI NOC Automation
Network Operations Centers handle massive volumes of alerts, logs, and events every day. Many alerts are repetitive, low-value, or poorly correlated, leading to alert fatigue and delayed responses.
We build agentic AI systems that continuously observe network telemetry, alarms, logs, and historical incidents. These agents correlate signals, identify probable root causes, recommend actions, and assist NOC teams in prioritizing issues.
The agents support engineers rather than replacing them. Human oversight, escalation logic, and approval controls are built in to ensure reliability and trust.
LQI (Link Quality Indicator) Parameter Check System
Monitoring link quality across complex network infrastructure requires continuous evaluation of multiple parameters such as latency, jitter, packet loss, and signal strength.
We design AI-driven LQI parameter check systems that automatically analyze link performance data, detect degradation patterns, and flag potential issues before service impact occurs.
These systems help operations teams maintain service quality, reduce manual checks, and proactively address network issues.
Spam SMS Filtering
Telecom operators face growing challenges with spam and fraudulent messaging, which impacts customer trust and regulatory compliance.
We build AI-based spam SMS filtering systems that analyze message patterns, sender behavior, content signals, and historical data to identify and block unwanted messages in real time.
The models adapt over time as spam tactics evolve, while governance controls ensure transparency and regulatory alignment.
Network Infrastructure Predictive Maintenance
Unplanned failures in network infrastructure lead to service disruptions and high operational costs.
We develop predictive maintenance systems that analyze equipment telemetry, historical failures, environmental data, and maintenance records to predict potential asset failures.
These systems help teams plan maintenance proactively, extend asset life, and reduce emergency repairs without over-maintaining healthy infrastructure.
Network Traffic Forecasting
Accurate traffic forecasting is critical for capacity planning, congestion management, and service optimization.
We build AI-driven network traffic forecasting models that analyze historical usage, temporal patterns, events, and growth trends to predict future demand across regions and network segments.
These insights support better infrastructure planning, cost optimization, and service reliability.
Ticket Routing and Resolution System
Customer and operational tickets often move slowly due to manual triage, misrouting, and lack of context.
We design AI-powered ticket routing and resolution systems that analyze ticket content, historical resolution data, network context, and team availability to route issues accurately and suggest resolution steps.
This reduces resolution time, improves first-touch accuracy, and lowers operational burden on support teams.
Our Project References
Built a predictive model to find and resolve problem occurrences by analyzing data in real-time from a multitude of Base receiver stations (BTS), Radio Network controllers (RNC) and user equipment.
Built an AI-driven Self-Organizing Network (SON) tool for optimizing frequency selection and minimizing interference in unlicensed 5GHz bands for a leading telecom company.
Answering Frequently Asked Questions
Yes. Integration with existing OSS, BSS, monitoring platforms, and ticketing systems is a core part of our delivery approach.
We use signal correlation, historical context, and adaptive thresholds to reduce noise and focus attention on meaningful issues.
Our models and logic are designed to adapt as network configurations, traffic patterns, and technologies change.
We focus on metrics such as reduced downtime, faster resolution times, improved service quality, and lower operational costs.
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