Modern AI assistants are great up to a certain project size, but they struggle with true scale and context. Take GitHub Copilot: its advanced local index caps out at just 2,500 files. And while its remote indexing can handle much larger repositories, it still fundamentally just searches for text within your code.
Whether local or cloud-based, standard AI tools fall short for complex systems. The real answers rarely live in isolated code snippets. They live in the connections: which functions call which, how a specific module ties to one of thousands of Jira tickets, and where a Confluence doc explains an architecture decision made years ago.
That's why the Adam Project is built for scale. We don't just scan files—we map the context.
We built a knowledge graph that bridges the gap between massive repositories and endless ticket histories. When a developer asks a question, the system doesn't just return random files that contain your search terms, hoping it finds the relevant context. Instead, it gives you a full picture that traces the path from a feature request, through the related components, to similar past issues and their resolutions.
It pulls your code, Jira tickets, Confluence docs, and PRs into a single, connected view within minutes, regardless of the system's size.
The difference between locating a code snippet and understanding its full context—from the underlying Jira ticket to the final pull request—is the difference between a helpful coding companion and a system-level engineering assistant. And that's exactly what we're building. https://lnkd.in/gwC-Qtsb