Transforming
telecommunications
- Secure, flexible and cost-effective AI solutions by leveraging the latest AWS services
Explore telecommunications specific content including case studies & insights.
Advanced AWS partner
3x AI & ML competencies
We have proven expertise in our AI services which are being leveraged to transform industries.
AI is transforming the telecom industry. We leverage AI and data analytics to optimise performance, automate operations, and enhance customer experience.
By harnessing the power of AI, we enable telecom companies to make data-driven decisions, boost efficiency, and stay ahead of the competition in the cloud.
Featured case study
Benefits
Understanding the telecom industry
Cloud technology is revolutionising the telecom industry by enabling virtualisation, infrastructure sharing, and Network as a Service (NaaS). It offers improved scalability, flexibility, and cost efficiency, allowing you to quickly deploy and manage network services. Cloud-based solutions enhance the telecom experience through innovative services, data analytics, and insights. With reduced upfront investments and optimised resource utilisation, cloud technology is transforming the telecom industry by enabling agility, cost savings, and adaptation to the evolving digital landscape.
Virtualisation
The global network function virtualisation (NFV) market is projected to reach $70.57 billion by 2026.
researchandmarkets.com
Infrastructure sharing
Cost efficiency
Omnichannel retailing
Telecom use cases by solution
Introduce AI to your business
Unlock the transformative power of AI/ML in telecom with Firemind’s cloud solutions. Drive revenue growth, enhance customer experiences, and optimise operations. Leverage AI algorithms and machine learning in the cloud to gain insights, predict trends, and build effective infrastructures. Stay ahead, exceed expectations, and achieve success with scalable, cost-effective, and agile cloud solutions.
01
Network optimisation
Hyper quality – AI/ML algorithms optimise network performance, predict congestion, optimise routing and allocate resources for better reliability and quality of service.
02
Predictive maintenance
03
Experience enhancement
Chatbots – AI powered chatbots and virtual assistants offer personalised customer support by understanding queries, providing instant responses, and performing tasks like bill inquires and troubleshooting, enhancing efficiency and customer satisfaction.
04
Fraud detection
Super secure – By analysing call records, data usage, and billing information, AU algorithms detect suspicious behaviours like SIM card cloning and identity theft, aiding operators in combating fraud and enhancing security measures.
05
Virtual networks
Built intelligently – AI/Ml technologies enable intelligent virtual networks that self-optimise, self-heal and adapt to changing traffic patterns. ML algorithms learn from user behaviours, predict demand fluctuations, and allocate resources for optimal network performance.
06
Network planning
Best coverage – AI/ML algorithms optimise network planning by analysing data like geography, population density, and user behaviour. This leads to efficient resource utilisation, improved network performance, and optimised coverage and capacity planning.
07
Revenue assurance
Fraud management – AI detects and prevents revenue leakage and fraud by analysing data from billing systems, call records, and transactions. It identifies errors, unauthorised usage, and subscription abuse, safeguarding telecom operator’s revenue streams.
Implement AI into your industry workflow
Explore & define
AI roadmap: Exploration, applications & goal setting
We’ll explore AI’s applications and benefits through research, workshops, and team engagement. This will identify how AI can enhance customer experience, inventory management, demand forecasting, pricing optimisation, and fraud detection. We’ll then set clear goals, create a roadmap, and identify specific use cases for integrating AI in your retail operations.
Assess & develop
Data preparation: Evaluation & model development
We’ll evaluate data sources, plan infrastructure, and aggregate diverse data. Then, we’ll develop AI models through machine learning, including algorithm selection, feature engineering, training, and validation. Models will be deployed, integrated, and tested for accuracy and performance.
Refine & scale
AI expansion: Monitoring, retraining & scaling
AI implementation is iterative, involving continuous monitoring, feedback collection, and refinements. Retraining models with more data enhances accuracy and adaptability. After successful initial deployment, expand AI across departments with chatbots for customer support, computer vision for visual product search, and AI-driven recommendation engines.
