Graphify turns codebases into queryable knowledge graphs

This is the kind of infrastructure layer that can make AI coding assistants feel meaningfully less forgetful, especially in large repos where structure matters more than keyword overlap.

• –The strongest pitch is not “AI reads more files,” but “AI reasons over relationships,” which is a better fit for architecture questions, refactors, and dependency tracing.
• –Multi-modal ingestion is a real differentiator: code, Markdown, PDFs, and diagrams in one graph is much more useful than plain chunked RAG.
• –The token-reduction numbers are compelling, but the bigger win is persistence: once the graph exists, follow-up questions should stay cheaper and more consistent.
• –The main risk is workflow friction; this only pays off if teams actually keep the graph updated and trust it as the first stop for repo context.
• –It sits in the same broad lane as code intelligence and agent memory tooling, but with a more explicit structural model than vector search alone.