From data overload to actionable insights: How Verizon Connect scaled agentic AI to 100,000 users

Verizon Connect scaled an agentic AI solution to support 100,000 users daily, leveraging a hybrid architecture that combines real-time data processing with batch processing for historical data analysis. The solution uses a stateful retrieval model to retrieve relevant data from a knowledge graph, enabling users to ask natural language questions and receive accurate and timely insights. The agentic AI system is built on top of a scalable and secure infrastructure, allowing for seamless integration with existing systems and data sources. This approach enables Verizon Connect to provide clear and actionable insights to its users, driving business growth and operational efficiency.

⚡ Key Takeaways

• The agentic AI solution supports 100,000 users daily, demonstrating its scalability and reliability.
• The hybrid architecture combines real-time data processing with batch processing for historical data analysis, enabling efficient data processing and analysis.
• The stateful retrieval model is used to retrieve relevant data from a knowledge graph, supporting natural language queries and accurate insights.
• The solution is built on top of a scalable and secure infrastructure, ensuring seamless integration with existing systems and data sources.
• The agentic AI system relies on a robust data pipeline, which may require significant upfront investment and maintenance.
• WhyItMatters: This agentic AI solution can be applied to various industries that deal with large amounts of fleet data, such as logistics, transportation, and delivery services, enabling them to provide clear and actionable insights to their users and drive business growth.
• TechnicalLevel: Intermediate
• TargetAudience: ML Engineers
• PracticalSteps:
• Assess your existing data pipeline and infrastructure to determine the feasibility of implementing a hybrid architecture.
• Design a knowledge graph that can support stateful retrieval models and natural language queries.
• Integrate the agentic AI solution with your existing systems and data sources, ensuring seamless integration and scalability.
• ToolsMentioned: None
• Tags: AGENTS, INFERENCE

This agentic AI solution can be applied to various industries that deal with large amounts of fleet data, such as logistics, transportation, and delivery services, enabling them to provide clear and actionable insights to their users and drive business growth.

✅ Practical Steps

1. Assess your existing data pipeline and infrastructure to determine the feasibility of implementing a hybrid architecture.
2. Design a knowledge graph that can support stateful retrieval models and natural language queries.
3. Integrate the agentic AI solution with your existing systems and data sources, ensuring seamless integration and scalability.

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