Stenography is an AI-powered documentation assistant designed for developers who want clean, accurate, and always up-to-date code comments with minimal effort. By analyzing your source code directly, Stenography automatically generates human-readable explanations, inline comments, and high-level summaries that match how real engineers think and talk about systems. Instead of manually writing and maintaining documentation that quickly drifts out of date, you can let Stenography keep your codebase understandable as it evolves. Stenography plugs into your existing workflow, working alongside your editor, terminal, and version control tools. It understands modern programming languages, patterns, and frameworks, helping you document everything from small utility functions to complex APIs and architectural modules. The tool is especially valuable for teams onboarding new developers, sharing context across time zones, or maintaining legacy code that lacks clear explanations. With automatic documentation suggestions, you stay in the flow of coding while still producing high-quality docs as a natural byproduct of your work. Stenography can generate explanations on demand, highlight confusing sections, and offer consistent documentation styles across your repositories. Whether you are a solo developer or part of a large engineering organization, Stenography helps reduce knowledge silos, speed up code reviews, and make your codebase easier to understand for future you and everyone else.
Generate clear docstrings and inline comments for existing codebases that were written quickly and never properly documented.
Help new team members understand unfamiliar services, APIs, or legacy modules by auto-generating readable overviews and usage notes.
Speed up code review by attaching concise AI explanations to complex pull requests so reviewers can grasp intent faster.
Improve internal libraries and SDKs with consistent, high-quality documentation to reduce support questions from other teams.
Support refactoring efforts by documenting current behavior before making changes, reducing the risk of breaking hidden assumptions.