What problem PViz is solving
Large codebases become hard to reason about because structure is implicit
Most onboarding approaches operate at the wrong level: reading files sequentially, grepping for symbols, asking teammates, or pasting snippets into AI. You accumulate details without first establishing context — where the complexity lives, what depends on what, and where execution begins.
PViz extracts structural signals directly from the repository to restore orientation: central vs peripheral modules, dependency paths, coupling hotspots, and cycles.
The same problem applies when using LLMs for code work. Pasting fragments gets you fragment-level answers — confident, plausible, and often wrong. A compressed structural bundle gives your LLM the full picture, which means fewer hallucinations, higher-fidelity responses, and answers that are actually grounded in your codebase.
What PViz does (and does not do)
Narrow scope by design
- ✓Map dependency relationships
- ✓Surface coupling hotspots
- ✓Detect circular dependencies
- ✓Identify entry points
- ✓Group code into zones
- ✓Export LLM-ready bundles
- ✕Infer business intent
- ✕Explain domain logic
- ✕Write documentation for you
- ✕Claim architectural correctness
- ✕Replace engineering judgment
- ✕Guess missing context
PViz produces evidence about structure. How you act on it is intentionally left to you.
Who built it
A pragmatic origin story (no résumé cosplay)
PViz was built by a solo builder who is not a programmer by trade.
It started with a one-time problem: dealing with a program that got large enough that normal "read files and figure it out" approaches stopped working. The issue wasn't syntax or missing documentation — it was not having a reliable way to see how the system fit together.
AI tools were used heavily during development, and PViz is designed for AI-assisted analysis as well. The output artifacts give you accurate, architecture-aware answers from your LLM instead of confident hallucinations based on whatever fragments you happened to paste in.
The guiding principle has been consistent: make implicit structure explicit, then let understanding follow.
Intended use
Where PViz tends to help the most
- ✓Getting accurate, grounded answers from your LLM instead of hallucinated guesses
- ✓Joining a new team or inheriting a legacy system
- ✓Evaluating open-source projects or third-party libraries
- ✓Planning refactors and understanding change impact
- ✓Identifying coupling hotspots and circular dependencies
Privacy and safety
Designed to minimize risk when analyzing code
- ✓Analysis runs in isolated job environments
- ✓Private repo access uses user-provided credentials only for the job
- ✓Artifacts are generated per job and per user
- ✓No cross-user sharing of repositories or outputs
If you're evaluating PViz for sensitive code, review the privacy/terms pages and use a limited-scope repo first.
Want the map before you dive into the code?
See what PViz found in the FastAPI codebase across 9 versions — dependency graphs, coupling signals, and architectural metrics — then try it on your own repo.