Sourcegraph case studies listing: AI-assisted code search, Cody, and Batch Changes across engineering teams
Engineering teams struggle to understand, navigate, fix, and automate code changes across large multi-repository codebases spanning multiple code hosts, often relying on teammates and spending months on tasks that could take days.
Engineering teams using Sourcegraph report measurable improvements in developer productivity, time savings, employee satisfaction, and the ability to address security vulnerabilities and large-scale code changes faster.
Frequently asked questions
What did this team achieve with this AI workflow?
Engineering teams using Sourcegraph report measurable improvements in developer productivity, time savings, employee satisfaction, and the ability to address security vulnerabilities and large-scale code changes faster.
What tools did this team use?
Sourcegraph, Cody, Batch Changes, Universal Code Search, Perforce, GitHub.
What results were reported?
Time for large-scale code changes: 80%; Developer productivity impact: measurable impact of developer productivity, time savings, and employee satisfaction; Repository analysis time: days instead of months (source-reported, not independently verified).
How is this it support AI workflow structured?
Multi-repo code navigation → AI code understanding → Agentic AI development → Automated large-scale changes.