How Did a Global Network Leader Cut Redundant Pings by 50% and Boost AI Experimentation by 30%?

Client

The client is a global leader in networking and IT infrastructure, with a diverse portfolio spanning networking hardware, software, telecommunications equipment, and advanced technology services. Their offerings include routers, switches, cybersecurity solutions, and IoT devices, supporting businesses and consumers worldwide. 

With the rapid rise of connected devices, fast-paced technological advancements, and growing cybersecurity threats, the industry is at a critical inflection point. To remain ahead of the curve, the client set out to strengthen its digital foundation and foster continuous innovation. As part of this vision, they aimed to establish a dedicated Co-Innovation Hub to develop, test, and scale AI-driven solutions.

Market Trends in the Networking and IT Infrastructure Industry

The networking and IT infrastructure sector is undergoing rapid change with the explosion of connected devices, rising cybersecurity threats, and the demand for seamless, high-speed connectivity. Enterprises are investing in AI-driven networks to manage complexity, enhance performance, and deliver secure, reliable digital experiences.

To stay competitive, organizations must build scalable AI pipelines and flexible innovation frameworks that support rapid experimentation and compliance with evolving regulations.

For the client, this meant laying a strong foundation to develop and scale AI solutions across multiple use cases. As a starting point, they focused on improving smart device callback management, where inaccurate estimates were causing delays, repeated pings, and unnecessary strain on both customer and client networks.

Business Challenges

The client needed to future-proof its AI strategy while addressing pressing operational inefficiencies. While the broader industry is moving toward AI-driven networks and intelligent device management, the client faced foundational gaps that limited its ability to innovate and scale effectively.

• Fragmented Foundation for AI Innovation: The absence of a unified, scalable platform made it difficult to experiment with and deploy AI/ML solutions across 50–80 potential use cases. This fragmentation slowed innovation, created silos, and made standardization across teams challenging.
• Inefficiencies in Device Callback Management: An immediate challenge emerged in smart device operations. Inaccurate callback time estimates caused delays, repeated pings, and unnecessary network strain, impacting both customer experience and overall network efficiency.

Use Case: Transforming Device Management: Real-Time ML Model for network callback time in the Smart Devices space.

LTM Solution

LTM partnered with the client to tackle the immediate smart device challenge while building a strong, future-ready foundation for AI innovation across the enterprise. The approach ensured quick wins while laying the groundwork for long-term scalability and standardization.

1. Solving the Immediate Business Problem

To reduce network strain and improve device performance, LTM developed a real-time machine learning model that accurately predicts device callback times.

• API-Driven Predictions: Connection requests are sent to the model via API, which returns precise callback estimates.
• Impact: Reduced redundant pings, minimized delays, and improved overall network efficiency and customer experience.

   2.   Establishing Scalable AI Foundations

To enable AI-driven innovation across 50–80 potential use cases, LTM implemented a standardized, enterprise-wide framework for AI development and deployment:

• Vertex AI Pipelines: Built and hosted end-to-end ML pipelines on GCP Vertex AI for development, deployment, and monitoring.
• Gen AI Stack: Leveraged GPT-4o with Weaviate vector database, hosted on-premises using CAE OpenShift clusters for secure, scalable innovation.
• Standardized Development Protocols: Adopted Python-based development with LangChain and LangGraph for modular, efficient builds.

   3.   Driving Continuous Innovation

Three reusable enterprise frameworks were created to accelerate future use cases:

• Use case evaluation framework for objective prioritization and scaling.
• MLOps framework for production-grade AI lifecycle management.
• RAG/agentic AI framework to support diverse gen AI use cases with speed and consistency.

This holistic approach resolved a critical operational bottleneck while empowering the client to scale AI solutions seamlessly across its global ecosystem.

Business Benefits

• Improved Network Efficiency

Reduced redundant pings by over 50% for problematic request fragments, easing network congestion and minimizing processing delays.

• Reliable, Low-Latency Performance

Maintained API response times under 50 milliseconds at the 99th percentile, ensuring seamless, real-time interactions.

• Accelerated AI Innovation

Enabled four additional AI use cases and boosted experimentation rates by ~30%, empowering teams to innovate faster and focus on strategic growth.

Conclusion

LTM helped the client move from concept to scale by first addressing a critical device management issue and then establishing a robust Co-Innovation Hub for AI. With reusable frameworks and scalable foundations in place, the client can now experiment, deploy, and operationalize AI solutions with speed and confidence.

Today, four solutions are live and one in staging, with many more in progress—paving the way for continuous innovation and enterprise-wide AI adoption. LTM remains a trusted partner, driving ongoing value and future-ready transformation.

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