Hyperscalers

Hyperscalers are grappling with challenges such as managing massive data volumes, ensuring robust cybersecurity, and maintaining energy efficiency. Additionally, they must navigate regulatory complexities and meet the growing demand for scalable and reliable cloud services. However, by leveraging advancements in artificial intelligence and edge computing, these challenges can be effectively addressed and transformed into opportunities for innovation and growth.

Common issues facing hyperscalers grappling with increased data demands

Security
Load balancing
Scaling and reliability
Security
Load balancing
Scaling and reliability

Security

Maintaining data integrity in such vast and complex environments is critical, especially as data volumes grow and more applications become dependent on real-time data analysis. Data corruption or loss can have severe repercussions, affecting customer trust and business operations.
Security
Load balancing
Scaling and reliability

Load balancing

Hyperscalers often grapple with the issue of load balancing, which is the process of evenly spreading out the computational workload among servers. This is vital to keep the system running smoothly and to avoid overburdening any single server, which could slow down the entire system.
Security
Load balancing
Scaling and reliability

Scaling and reliability

As customer data demands evolve and AI applications become more prevalent, hyperscalers are constantly met with the challenge of scaling their data center capabilities to meet new demands. Additionally, any downtime in these data centers can result in the loss of millions for the hyperscaler and its end customers. Ensuring your network is resilient and reliable requires investment in current technologies. 

Security

Maintaining data integrity in such vast and complex environments is critical, especially as data volumes grow and more applications become dependent on real-time data analysis. Data corruption or loss can have severe repercussions, affecting customer trust and business operations.

Load balancing

Hyperscalers often grapple with the issue of load balancing, which is the process of evenly spreading out the computational workload among servers. This is vital to keep the system running smoothly and to avoid overburdening any single server, which could slow down the entire system.

Scaling and reliability

As customer data demands evolve and AI applications become more prevalent, hyperscalers are constantly met with the challenge of scaling their data center capabilities to meet new demands. Additionally, any downtime in these data centers can result in the loss of millions for the hyperscaler and its end customers. Ensuring your network is resilient and reliable requires investment in current technologies. 

How Hyperscalers can benefit from AI 

Besides having robust security measures, using AI-powered cybersecurity tools to detect unusual activities in your system can add an extra layer of protection for your IT infrastructure's vital elements.
Using AI-driven algorithms and software, hyperscalers can orchestrate and automate the movement of data to ensure servers remain efficient and avoid overloading.
AI enhanced software can be used to streamline operations, from maintenance to software updates and security patches. Predictive analytics and machine learning are also in play, predicting and preventing potential failures or slowdowns to ensure services are always up and running.

Using AI to meet changing customer demands

A hyperscaler provides extensive cloud services to a diverse range of end customers, including enterprises, startups, and individual developers. These customers rely on the hyperscaler for scalable computing resources, storage, and advanced networking capabilities. However, as data volumes grow exponentially, the need for efficient data offloading and low-latency processing becomes critical. The corporation's end customers are experiencing increased latency and bandwidth constraints due to the massive amounts of data being generated and processed.
To address this, the hyperscaler partners with a trusted partner that specializes in computing solutions that offload data processing closer to the data source or the network's edge. The goal is to seamlessly integrate the hyperscaler's cloud infrastructure with its partner's computing capabilities, ensuring efficient data transfer, low latency and optimal performance.
1
Network optimization
IT teams work to implement AI algorithms to dynamically optimize network routes between the hyperscaler's data centers and its partner's edge nodes. These algorithms analyze real-time network conditions, predict congestion and reroute traffic to ensure minimal latency and maximum throughput.
2
Data compression
Compression algorithms powered by artificial intelligence minimize the volume of data before it's sent, ensuring more efficient bandwidth consumption and quicker data movement. This is especially useful when clients are fine-tuning their AI models and prefer to avoid uploading the full dataset to the cloud.
3
Threat detection
The hyperscaler deploys AI to monitor network traffic for unusual patterns or potential security threats. This ensures that data offloading and transmission are secure and protected from cyberattacks.
4
Real-time analytics
Finally, the hyperscaler can provide end customers with real-time analytics and insights into their data flow and network performance. AI-driven dashboards offer actionable recommendations to optimize their usage.
Aligning with its computing partner has positioned the hyperscaler to better address the evolving demands of managing a flexible digital infrastructure. This collaboration enhances the security of customer data, which is crucial for refining models and delivering valuable insights to clients.

Enhance overall efficiency and productivity

Networking solutions

Can greatly assist a hyperscaler in implementing and utilizing AI by providing robust, high-speed connectivity essential for handling vast amounts of data.
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Networking solutions

Can greatly assist a hyperscaler in implementing and utilizing AI by providing robust, high-speed connectivity essential for handling vast amounts of data.
These solutions ensure efficient data transfer between edge devices, data centers and cloud environments, reducing latency and enhancing real-time processing capabilities. By optimizing network performance, hyperscalers can support AI applications such as machine learning, data analytics and automated decision-making more effectively. This leads to improved operational efficiency, scalability, and the ability to deliver faster, more reliable AI-driven services to customers.

Edge solutions

Can significantly aid a hyperscaler in implementing and utilizing AI by bringing computation and data storage closer to the source of data generation.
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Edge solutions

Can significantly aid a hyperscaler in implementing and utilizing AI by bringing computation and data storage closer to the source of data generation.
This reduces latency and enhances real-time processing capabilities, which is crucial for applications like IoT, autonomous systems, and real-time analytics. Edge solutions ensure efficient data handling and processing at the network’s edge, reducing the load on central data centers. This approach enables hyperscalers to optimize their AI operations, improve performance, and deliver faster, more reliable services to their customers

Cybersecurity solutions

Are crucial for a hyperscaler implementing and utilizing AI, as they protect sensitive data and AI models from cyber threats.
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Cybersecurity solutions

A​re crucial for a hyperscaler implementing and utilizing AI, as they protect sensitive data and AI models from cyber threats.
These solutions ensure secure data transmission, safeguard against breaches, and maintain the integrity of AI algorithms. By integrating advanced security measures, hyperscalers can confidently deploy AI applications, knowing their infrastructure is protected. This not only enhances trust and reliability but also ensures compliance with regulatory standards, enabling hyperscalers to focus on innovation and operational efficiency without compromising security.

Managed and professional services

Can be invaluable for a hyperscaler looking to implement and utilize AI by providing specialized expertise and support tailored to large-scale operations.
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Managed and professional services

Can be invaluable for a hyperscaler looking to implement and utilize AI by providing specialized expertise and support tailored to large-scale operations.
These services can help identify strategic AI applications, such as optimizing data center operations, enhancing cybersecurity, and improving customer analytics. They ensure seamless integration with existing infrastructure, offer comprehensive training for technical teams, and provide continuous maintenance and optimization. This holistic approach enables hyperscalers to leverage AI effectively, driving innovation, efficiency, and scalability across their vast operations.

Other ways hyperscalers can utilize AI to improve operations

Energy consumption
AI can optimize energy usage by predicting demand and adjusting power consumption accordingly. For example, AI algorithms can analyze historical data and forecast energy needs, allowing for more efficient energy distribution and reducing waste.
Customer experience improvement
AI provides end customers with real-time analytics and insights into their data flow and network performance. AI-driven dashboards offer actionable recommendations to optimize their usage.
Rising costs
AI can help mitigate rising costs by automating routine security tasks, improving operational IT efficiency, and reducing the need for manual intervention. Eliminating the need for manual intervention allows the staff to shift their priorities to mission critical tasks and keeps labor costs down, leading to cost savings.

Capabilities

Hyperscalers are at the forefront of the AI era, leveraging cloud services, tools and platforms for their customers to enhance creativity and productivity and address significant challenges. Network, cybersecurity, edge fabric and expertise are critical to their strategy. This infrastructure is essential for handling the increasing data demands and ensuring a reliable and scalable platform for various customer workloads, including AI and high-performance computing. Even though hyperscalers already own these capabilities, strategic partnerships to help expand infrastructure empower hyperscalers for AI-driven future.

Networking

Problem to solve

Hyperscalers face significant challenges in implementing their AI strategies without robust networking infrastructure to support their operations and the ones of their customers. Establishing connections with AI models, data centers, and enterprise customers becomes difficult. The absence of networking infrastructure hinders the quick establishment of high-capacity connections necessary for the fast transfer of large datasets required for AI model training and inferencing used on multiple use cases like spreading computational overload between servers and improving cloud services to customers. As a result, data movement to AI models becomes slower and less efficient, compromising the speed and efficiency of AI initiatives. Additionally, networking is vital for organizing, cleaning, and preparing data for AI, and without it, the process of preparing data for AI processing becomes more complex and time-consuming.

Why networking solutions are critical

Efficient and rapid transmission is essential for hyperscalers to manage the substantial volumes of data streaming from the cloud or data center at extremely high speeds. This capability is vital for leveraging the full potential of AI in processes such as automating the movement of data, which helps servers maintain efficiency and prevent overloading. Reliable, dedicated and instantly scalable networking solutions are the key to minimizing latency and ensuring the ample bandwidth AI systems need for optimal performance. 
Networking solutions enable the efficient transfer of large volumes of data managed by hyperscalers. They support high-capacity data pipelines for training and deploying AI models, enabling integration and automation across various platforms. These solutions help hyperscalers reduce overall technology costs and concentrate on driving innovation to advance an AI-enabled world.

How networking solutions enable AI for hyperscalers

Networking solutions enable AI by providing the infrastructure required to manage large volumes of data and distribute computational load between entities such as public cloud datacenters, peering networks, enterprise users and sites, data pipelines, end customers and servers. By delivering cloud services to people efficiently, hyperscalers can enhance creativity and productivity while addressing some of humanity’s greatest challenges. These solutions enable seamless data processing, control over network traffic and high-bandwidth cloud connectivity without disrupting operations.
This enhances datacenters and AI-powered cloud services, improving efficiency, accuracy and business decision-making for millions of users and organizations. Networking solutions offer on-demand connectivity among all the components mentioned above, allowing hyperscalers to dynamically scale operations and meet fluctuating AI workload demands. They enable rapid, large bandwidth connections for swift data transfer needed for AI model training and inferencing. This accelerates data movement, enhancing the speed and efficiency of AI initiatives.
Additionally, networking assists in data classification, cleaning and staging so thatdata is ready and optimized for model training. These solutions support high-capacity data pipelines for training and deploying AI models, facilitating integration and automation across various platforms and environments, and make the network infrastructure seamless and invisible to users—allowing hyperscalers to focus on driving innovation, improve their offerings and create new solutions for  their global customer base. 

Conclusion

By optimizing data movement and classification, networking solutions help hyperscalers manage resources efficiently, reducing AI data management time and cost. Networking solutions provide critical proximity and location for deploying AI models, ensuring efficient data processing and access. They enable hyperscalers to execute AI strategies more effectively, maintain leadership in the market and meet the growing demand on their datacenters due to AI. The efficiency gained translates into a competitive edge, allowing hyperscalers to enhance AI-driven services for customers and optimize operational efficiency. 
Robust connections between hyperscalers, AI models, data centers and enterprise customers are critical for timely AI processing. Networking addresses the challenges of data availability, movement and staging, enabling hyperscalers to execute their AI strategies more effectively. 

Cybersecurity

Problem to solve

As critical actors in the AI revolution, hyperscalers manage an immense volume of sensitive dat, from their internal operations as well as confidential data from their global customers. Ensuring the integrity, confidentiality and availability of this data while protecting it from unauthorized access and cyberthreats, is a top priority. While hyperscalers already possess and even provide cybersecurity solutions, expanding these measures is critical to ensure the continuity of their AI operations. Robust and scalable cybersecurity infrastructure can help prevent breaches that compromise the confidentiality and integrity of sensitive information. Cybersecurity threats could disrupt AI operations, corrupt AI models, steal intellectual property and lead to downtime that will affect millions of users worldwide. Inadequate cybersecurity measures could erode trust among users and clients, impacting the hyperscalers’ reputation and ability to attract and retain customers. Additionally, failing to maintain a secure AI environment could result in non-compliance with data protection regulations such as ISO 27001, GDPR (Europe) and GLBA (financial services), leading to legal and financial repercussions.

Why cybersecurity infrastructure is  critical for AI

The preservation of customer and organizational data integrity, confidentiality and availability is contingent upon the implementation of the most stringent cybersecurity standards. Protecting data, whether at rest or in transit, and safeguarding AI-driven processes used by hyperscalers to provide personalized, high-value services to their customers from unauthorized access, model manipulations and cyberthreats is of paramount importance. 
Hyperscalers might need more cybersecurity infrastructure beyond what they already have for several reasons. Enhanced security intelligence from external sources could provide broader threat visibility and enhance overall security posture. Securing vast, complex infrastructures requires comprehensive threat coverage, and additional cybersecurity infrastructure could help cover potential gaps left by existing solutions, ensuring a more robust defense mechanism.
Without robust cybersecurity measures, the system could be vulnerable to cyberattacks. Unauthorized access to the system could lead to manipulation of resource allocation data, causing inefficiencies and potential service outages. Additionally, sensitive operational data could be exposed, leading to financial losses, legal repercussions, and damage to the company's reputation.
The cybersecurity landscape is constantly evolving, now even more with new AI-powered threats emerging regularly. Additional infrastructure could offer more agile and adaptive security measures to effectively counter evolving threats and continue innovating for customers, supporting their critical roles in ushering in the era of AI. Additionally, advanced threat intelligence that benefits from direct visibility from global networks is a powerful component in the cybersecurity strategy, as it is critical for proactive threat detection and prevention and could complement the intelligence and signals that hyperscalers already have. As regulations around data protection and privacy become more stringent, hyperscalers may need to bolster their cybersecurity infrastructure to ensure compliance with ISO 27001, GDPR (Europe), GLBA (financial services), and other evolving legal requirements.

How cybersecurity enables AI for hyperscalers

Cybersecurity solutions offer essential protection for the advanced AI technologies used by hyperscalers, enabling them to continue providing high-quality services to customers and partners, accelerating technology innovation and securely transforming businesses with AI. 
While hyperscalers already possess comprehensive cybersecurity measures and partnerships with major security control providers, expanding these measures can further secure their infrastructure against potential threats. This ensures the integrity and confidentiality of AI data and models. When threat intelligence benefits from direct access to deeply peered global networks and is coupled with powerful AI/ML engines and top human expertise, it enables the identification of new adversaries as they emerge. This threat intelligence can be integrated into security solutions to rapidly detect and neutralize internet-based threats, enhancing capabilities across various platforms.
Ensuring a secure environment with enhanced cybersecurity infrastructure supports AI technologies' continuous innovation and development. This enables hyperscalers to explore new AI applications, empower their users to be more creative and productive and help solve some of humanity's biggest challenges, all without compromising security.

Conclusion

Hyperscalers operate on a massive scale, and their cybersecurity needs can change rapidly. Additional infrastructure offers the scalability and flexibility needed to adapt to new threats, protect growing AI operations and provide customers with a secure, cloud-native, high-performance computing platform*. Advanced threat detection and response capabilities are essential for identifying and mitigating threats that could compromise their AI systems and impact their reputation.
Ensuring a secure environment with additional cybersecurity infrastructure supports the continuous innovation and development of AI technologies. This enables hyperscalers to explore new AI applications without compromising security. 

Edge Fabric

Problem to solve

As AI initiatives take off, the influx of data requires an infrastructure strategy for secure connectivity, storage, acces, and transmission of that data for real-time processing and decision making. Traditional networks and infrastructure simply can’t keep up with the demand. 
For hyperscalers, AI systems necessitate minimal latency and real-time local processing to meet the demands of their internal operations and their growing customer base like real-time analytics and insights and AI-driven dashboards. Limited scalability might hinder their ability to efficiently scale operations to meet growing demand, particularly for AI and data-intensive applications. Reduced performance, including higher latency and slower data processing, can affect the efficiency of hyperscalers' AI models. This can cause bottlenecks as data travels across a dispersed network to a data center and back, potentially leading to less effective performance and customer experiences. Existing infrastructure will struggle to keep pace. Delays and inefficiencies would become commonplace, severely impacting the speed and accuracy of critical operations. Furthermore, without access to advanced edge fabric capabilities, hyperscalers could face constraints in innovation, limiting their ability to develop and deploy new AI-driven services and solutions.

Why edge fabric infrastructure is critical for AI

Edge fabric solutions are instrumental in enhancing the performance of AI-powered systems used by hyperscalers in their internal operations like optimizing routes and flow of data for improved performance, or adding value to their customers with real-time insights. By allocating computing resources close to data sources, they keep data local, reduce latency and boost processing speeds. This approach is pivotal in supporting a range of customer use cases where real-time processing is critical, including IoT, smart manufacturing, autonomous driving and smart retail. 
Without an edge fabric, each data center or node may strugglewith efficient dataprocessing locally. This can result in delays in analyzing resource usage and making real-time adjustments, as data must be sent to centralized servers for processing. The lack of local processing can increase latency, reduce system performance, and lead to higher bandwidth usage and costs. It may also cause bottlenecks in resource allocation processes, impacting overall operational efficiency and service quality.
Although hyperscalers already utilize edge fabric architectures, ensuring additional infrastructure is critical to keeping up with rapid advancements in AI technologies. Hyperscalers play a pivotal role in this transformation by providing scalable and robust infrastructure necessary for training and deploying complex AI models. This continuous expansion supports the development of innovative AI applications, enabling breakthroughs in areas such as natural language processing, computer vision and predictive analytics. Cost efficiency is another critical benefit. Efficient data processing and reduced data transfer costs through edge computing can lead to significant cost savings for hyperscalers, optimizing their overall operational expenses. These points underscore the importance of expanded edge fabric infrastructure in supporting the operational efficiency, security and strategic growth objectives of hyperscalers.

How edge fabric enables AI for hyperscalers

For organizations looking for a flexible digital infrastructure with low latency, speed and security, a network connectivity fabric can be customized to leverage existing lit fiber network, expand new routes to connect to the datacenter, and consumed or reconfigured on demand as data demands increase.
Edge fabric solutions enable AI by providing the necessary infrastructure to allocate computing resources close to data sources. This keeps data local, reduces latency and boosts processing speeds, which is essential for real-time analysis and decision-making. These capabilities are critical for machine learning model training, large-scale AI application deployment, data extraction and processing automation, and operational management and optimization—key operations for hyperscalers.
Additionally, edge fabric solutions enhance data sovereignty and compliance while containing the security footprint, ensuring that advanced AI technologies operate efficiently and effectively. A robust edge fabric infrastructure offers the scalability and flexibility needed to handle increasing data volumes and complex workloads, supporting the rapid growth of hyperscaler platforms and their customers.
With greater control over edge fabric infrastructure, hyperscalers can implement robust security measures tailored to specific needs, ensuring better protection of sensitive data and compliance with regulatory requirements. Access to advanced edge edge fabric infrastructure enables hyperscalers to develop and deploy innovative services and solutions, maintaining a competitive edge in the market, and keep playing the leading role in ushering in the era of AI.
Efficient real-time data processing and reduced data transfer costs through edge computing can lead to significant cost savings for hyperscalers, optimizing their overall operational expenses.

Conclusion

By maintaining a flexible and scalable infrastructure, hyperscalers can meet the evolving demands of AI workloads, thereby accelerating the pace of AI innovation and adoption across various industries. Access to advanced edge fabric infrastructure enables hyperscalers to develop and deploy innovative services and solutions that provide real-time analytics and enable IoT efficiently. Efficient data processing and reduced data transfer costs through edge computing can lead to significant cost savings for hyperscalers, optimizing their overall operational expenses.

Managed & Professional Services

Problem to solve

Given the volume of data and the complexity of processes and systems, hyperscalers face significant challenges in identifying and maintaining the appropriate infrastructure for their AI systems to operate efficiently. Ensuring that the infrastructure AI systems require is correctly deployed and seamlessly integrated into internal operations is crucial for optimal performance. Access to a diverse talent pool is essential for innovation and keeping pace with the rapidly evolving technology landscape. Hyperscalers may struggle to expand services and infrastructure without skilled professionals, potentially limiting their growth and competitiveness. A talent shortage could hinder their ability to scale operations efficiently and meet customer demands and could lead to decreased customer satisfaction, as customers may experience lower service quality, longer response times and limited offerings.

Why managed and professional services are critical for AI

Managed and professional Services (M&P) are critical for hyperscalers because, despite having the necessary talent, they cannot scale to meet all customer demands across various sectors. This scalability challenge can be addressed by leveraging M&P services, which help hyperscalers efficiently manage and expand their operations to cater to the growing and diverse needs of their global customer base.
By utilizing M&P services, hyperscalers can ensure that the infrastructure their AI systems require is deployed correctly and operating efficiently, maximizing performance and uptime. This allows internal resources to focus on core innovation strategies, ultimately enhancing the value provided to customers with their cloud services.

How managed and professional services enable AI for hyperscalers

Managed and professional services (M&P) are essential for enabling AI by providing expert guidance and tailored assessments. These services ensure that the infrastructure required for AI systems is correctly deployed, seamlessly integrated into internal operations and running efficiently. This maximizes performance and uptime, allowing internal resources to focus on core hyperscale activities such as delivering cloud computing services, intelligent document processing and operational efficiency to customers. This enhances the effectiveness of AI technologies and fosters trust and confidence among hyperscale leaders and stakeholders.
The critical role of M&P services in enabling AI for hyperscalers cannot be overstated. While hyperscalers possess talent, they face challenges in scaling their services to meet multiple user demands. M&P services help hyperscalers efficiently manage and scale their operations, ensuring they can meet the growing and diverse needs of their customer base. This is particularly important as hyperscalers navigate the complexities of deploying AI solutions across various sectors.

Conclusion

The deployment of AI solutions involves complex integration and management tasks that hyperscalers may struggle to handle alone due to the sheer scale of operations. While they have the necessary talent, scaling their services to meet the diverse and growing demands of their customer base is a challenge. Managed and professional (M&P) services can bridge this gap by providing the additional resources and expertise needed to efficiently manage and scale operations.
By leveraging M&P services, hyperscalers can ensure that their AI systems are operating at their best, empowering their customers to be more creative and productive. This allows internal resources to focus on innovation projects, ultimately enhancing the value provided to the global customer base. M&P services enable hyperscalers to concentrate on their core competencies and innovation in AI, while outsourcing the management and operational aspects to experts in the field. This approach accelerates the development and deployment of AI solutions without being bogged down by the intricacies of infrastructure management and support.

Our expertise at a glance

As a trailblazer in global networking for hyperscalers, Lumen is your strategic ally in the AI era. We are the only provider that can offer AI-ready infrastructure with broad coverage and unique fiber network routes, providing capacity and flexibility to meet your dynamic hyperscaler demands. Our forward-thinking solutions span network, cybersecurity and Edge Fabric suites, supported by a team of experts to optimize your network performance and security.

Reliable network for hyperscalers

  • Expertise: Leverage Lumen knowledge and experience in AI and load balancing to optimize your hyperscale environment.
  • Seamless integration: Enable smooth integration of AI tools with existing hyperscale infrastructure.
  • Scalability: Get scalable solutions that grow with your hyperscale needs, helping your network handle increasing demands.

Robust cybersecurity measures for hyperscalers

  • Specialization: Leverage our expert knowledge in AI and cybersecurity tailored specifically for hyperscale environments.
  • Seamless integration: Enable smooth integration of AI-driven cybersecurity tools with existing hyperscale infrastructure.
  • Enhanced support: Take advantage of dedicated support and resources to address unique security challenges faced by hyperscalers.

Edge fabric for hyperscalers

  • Minimal latency and real-time processing: Enable efficient data transfer and processing to meet the high demands of hyperscale environments and customer expectations.
  • Scalability and flexibility: Support the growth of AI and data-intensive applications with scalable solutions tailored for hyperscalers.
  • Enhanced performance and cost savings: Optimize data flow and minimize operational expenses in large-scale deployments.
  • Compliance and security: Enhance data sovereignty and provide robust security measures to meet stringent hyperscale operational requirements.

AI-ready infrastructure solutions

Networking Solutions
Unleash your business potential with networking solutions that enable inteligent automation, real-time performance data and dynamic bandwidth scalability.
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Security Solutions
Secure your digital environments and maximize productivity with award-winning cyber defense built for today's digital security landscape.
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Edge Private Cloud
Fully managed, pre-built infrastructure for high-performance private computing connected to our global fiber network.
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Managed & Professional Services
Proven technology expertise and results you can trust.
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Why Lumen

By partnering with Lumen, you can ensure that your AI initiatives are built on a solid foundation, enabling you to unlock new growth opportunities and stay competitive in a rapidly evolving digital landscape.

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