Global Semiconductor Leader: Transforming Bug Triage and Engineering Operations with GraphRAG
Faster Bug Correlation
Identify related issues, dependencies, and root causes across millions of bug reports.
Scalable GraphRAG Foundation
Support engineering knowledge graphs exceeding 100K+ nodes and edges without sacrificing performance.
Faster Engineering Investigations
Enable GraphRAG-driven troubleshooting, debugging, and operational intelligence at scale.
Overview
As this global semiconductor leader’s engineering operations expanded, they grew to manage millions of bug reports across products, systems, and teams, increasing the complexity of bug triage, troubleshooting, and root cause analysis.
By building on the Arango Contextual Data Platform, they created a live contextual data layer that connects bug reports, dependencies, and engineering knowledge. The result is faster bug correlation, improved issue analysis, and a scalable foundation for GraphRAG-powered engineering operations.
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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 this global semiconductor leader’s 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, his global semiconductor leader needed a live contextual data layer capable of connecting and analyzing engineering context across its growing ecosystem of operational data.
The Solution
The global semiconductor leader 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, the global semiconductor leader 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.
Across millions of bug reports, our engineers need to connect dependencies and historical knowledge to identify root causes. Arango helps us uncover relationships that keyword and vector search alone can miss, accelerating complex investigations and providing a scalable foundation for GraphRAG.
— Distinguished Engineer, Global Semiconductor Leader
Accelerating Bug Triage and Root Cause Analysis
By connecting bug reports, dependencies, and engineering knowledge into a live contextual data layer, The global semiconductor leader 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
The global semiconductor leader 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, they 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
Without Arango
- Engineering knowledge distributed across multiple systems
- Limited visibility into relationships between issues and dependencies
- Investigation bottlenecks during bug analysis
- Difficulty uncovering hidden operational context
- Traditional search and retrieval limitations
With Arango
- Live contextual data layer connecting engineering knowledge
- Faster bug correlation and issue analysis
- GraphRAG-powered engineering intelligence
- Improved access to operational context
- Support for knowledge graphs exceeding 100K+ nodes and edges
- Scalable foundation for AI-powered engineering operations
Why Arango
This global semiconductor leader 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, the global semiconductor leader can accelerate troubleshooting, improve issue resolution, and build a foundation for GraphRAG-powered engineering operations at scale.
