PViz compresses any repository into a structured analysis bundle that fits within your LLM's context window — so you get accurate, architecture-aware answers instead of confident guesses based on whatever files you happened to paste.
Dependency graphs, cycle detection, architectural metrics, semantic symbol inventories, sliceable outputs, and LLM-ready bundles for Python, TypeScript, JavaScript, Go, Java, Kotlin, Rust, and Ruby — in minutes.
Large codebases exceed LLM context limits. You paste partial files, get partial answers, and make decisions based on incomplete context.
PViz generates a compressed structural bundle of your entire repo — architecture, dependencies, metrics, and focused slices — so you get higher-fidelity answers grounded in your actual codebase, not hallucinations based on fragments.
PViz generates a complete structural analysis bundle, focused slices, and optional AI reports so you can orient, assess risk, and plan changes with confidence.
Build a cross-repo graph of modules, packages, and dependencies to understand architecture at a glance.
Organize large repositories into meaningful zones so you can reason about systems, boundaries, and change impact.
Get a clear breakdown of code size by language, directory, and hotspots to prioritize attention.
Identify dependency cycles and high-coupling areas that may slow refactors and increase change risk.
Break large analysis bundles into focused artifacts for nodes, edges, cycles, directories, tests, hubs, entrypoints, blast radius, semantic facts, and symbol inventories.
Export whole-codebase bundles or focused slices into Claude, GPT, or Cursor — and get responses grounded in your real architecture instead of guesswork.
Ask up to 5 questions and receive answers grounded in the PViz bundle — plus a concise evidence appendix.
Browse our FastAPI longitudinal analysis — 9 versions, full dependency graphs, and architectural metrics generated by PViz.