NVIDIA: Transforming Bug Triage and Engineering Operations with GraphRAG

The Challenge

Modern engineering organizations generate enormous volumes of operational data. Bug reports, dependencies, issue histories, engineering documentation, and troubleshooting knowledge are often distributed across multiple systems, making it difficult to uncover the relationships needed to resolve issues efficiently.

As NVIDIA’s NVBugs platform scaled to millions of bug reports, engineering teams needed a better way to identify root causes, correlate related issues, and surface operational context hidden across engineering systems.

Traditional search and retrieval approaches could help locate information, but they often struggled to capture the complex relationships that connect issues, dependencies, systems, and historical engineering knowledge.

To improve troubleshooting and debugging at scale, NVIDIA needed a live contextual data layer capable of connecting and analyzing engineering context across its growing ecosystem of operational data.

The Solution

NVIDIA built a live contextual data layer using the Arango Contextual Data Platform to connect bug reports, dependencies, engineering knowledge, and operational context, enabling GraphRAG-powered troubleshooting and issue analysis at scale.

Using the Arango Contextual Data Platform, NVIDIA combines graph relationships, vector search, and Deep Search into a unified model of engineering knowledge. This allows engineering teams to move beyond traditional search and retrieval approaches to uncover hidden relationships, identify related issues, and access the context needed to accelerate investigations.

The platform also provides a scalable foundation for future AI-powered engineering operations.

Accelerating Bug Triage and Root Cause Analysis

By connecting bug reports, dependencies, and engineering knowledge into a live contextual data layer, NVIDIA can more efficiently identify relationships that may not be visible through traditional search approaches.

Engineering teams can correlate related issues, investigate dependencies, surface relevant historical knowledge, and better understand the factors contributing to complex problems. This helps reduce investigation bottlenecks and accelerates the process of identifying and resolving issues.

Building GraphRAG-Powered Engineering Operations

NVIDIA is building the foundation for the next generation of AI-powered engineering operations.

The live contextual data layer preserves relationships across engineering systems, enabling AI applications to access richer context and deliver more accurate answers. By combining graph relationships with vector search and Deep Search, NVIDIA can provide engineering teams with more complete and explainable operational intelligence.

This foundation supports future innovations in debugging, troubleshooting, engineering productivity, and product quality.

Results

Why Arango

NVIDIA selected the Arango Contextual Data Platform because it provides a unified platform for building a live contextual data layer that connects relationships, search, analytics, and AI without requiring multiple specialized systems.

Built on Arango’s multimodel graph database, the platform enables organizations to connect and analyze complex relationships across bug reports, dependencies, engineering knowledge, and operational systems while simplifying infrastructure.

By preserving context across engineering workflows, NVIDIA can accelerate troubleshooting, improve issue resolution, and build a foundation for GraphRAG-powered engineering operations at scale.