WWT AI Network Assistant: This is an AI-powered tool that automates network troubleshooting, configuration validation and compliance checks for enterprise IT environments. It translates natural language queries into CLI commands, executes them on network devices via SSH, analyzes outputs using AI and provides actionable insights. Additionally, it integrates with Cisco documentation for reference and supports automated runbook execution to streamline network operations.

Daily Ops Summary Agent: This agent uses AI-powered summarization to bring the most relevant information from Incident and Change management into an intelligent report for operation specialists. It uncovers insights and trends buried in service management data, giving operations staff clearer visibility into performance, patterns, and potential improvements.

Incident Knowledge Assistant: This assistant provides fast, targeted guidance for IT Operations specialists. It uses representative data from an IT Service Management Platform to identify the most relevant information from Incident, Change, and Problem Management, as well as the Knowledge Base. This helps IT teams quickly triage and resolve trouble tickets, improving efficiency and reducing downtime.

Intelligent Resource Optimizer: This AI agent is designed to proactively manage and optimize computing infrastructure. It moves beyond static analysis to provide proactive infrastructure management. A specialized Time Series model is used to analyze historical system utilization and accurately forecast future resource demands. This enables IT Operations teams to receive data-driven recommendations for scaling and optimization of computing equipment, ensuring peak performance and cost-efficiency across the environment.

The RAG based digital human will be able to interact with users to answer questions on the hydrated data set. This data can be dynamically changed to create a fresh experience. This version of the Digital Human is launchable in our AIPG and can be interacted with during in-person tours.

LLM Clinical Trail Matcher: The recruitment of patients for clinical trials presents significant challenges for pharmaceutical companies, often delaying drug development, increasing cost and leading to study failure. This demo explores how AI applied to anonymized health records enables efficient trial matching —at single, bulk and multi-trial levels — reducing time, improving accuracy, and enhancing access for researchers, providers and patients.

The NVIDIA Data Flywheel Blueprint is designed to continuously improve AI models using real-world data. It starts with a large, high-accuracy model running in a central environment, captures usage data and difficult examples, retrains and fine-tunes the model, and then produces smaller, optimized models that can be deployed closer to where work happens. WWT runs this core process on Cisco infrastructure and then generates right-sized models for edge environments. This reduces latency and hardware requirements while maintaining model accuracy. 

The collaboration culminated in the Pre-Visit Planning Tool (PVPT), a demo designed to show how AI and hardware acceleration can work together to simplify the patient review process. The tool curates imaging, pathology and treatment data into a unified longitudinal summary, helping clinicians see the whole story of a patient's care in seconds rather than minutes.