Swimm is a SaaS product that automatically documents your codebase using AI. It keeps documentation up to date with changes, facilitates code understanding, and integrates seamlessly with existing tools. It supports various programming languages and is designed to enhance developer productivity by ensuring comprehensive, up-to-date code documentation.
Automatically document your entire codebase, from legacy to modern.
Documentation updates itself with every code change to keep your knowledge base current.
Understand context around the code with AI to facilitate knowledge transfer and onboarding.
Integrates seamlessly into your workflow with popular tools like GitHub, VS Code, and more.
Supports language-specific, including legacy languages, and automatically formats code into tutorials or guides.
Helps in updating and improving older code bases to enhance efficiency and compatibility.
Provides a structured way to create and manage code documentation, making it easier for developers to understand and utilize.
Supports the integration and utilization of AI technologies to improve workflows and processes.
Offers tools and strategies to efficiently manage and sustain older code bases, ensuring they remain functional and relevant.
Automatically ensures documentation is synchronized with code changes to prevent outdated information.
Leverages best practices and AI to enhance the quality of existing documentation.
Utilizes protocols and architecture to automatically generate relevant documentation based on code changes and dependencies.
Employs AI to answer developer questions, providing context and solutions instantly as they work.
Encourages a documentation-first approach, streamlining the process of writing and maintaining documentation through integrated tools and processes.
Automatically generate and backfill missing documentation to keep code consistently documented.
Create visual diagrams of the system and code to maintain a clear overview and understanding.
Analyze legacy code to understand its structure and relations, facilitating modernization and maintenance.
Ensure the code is backed by high-quality documentation for better code reliability.
TechDocs is a docs-as-code solution built directly into Backstage by Spotify. It helps developers create and discover technical documentation using sources like Confluence, Google Docs, or README files, and surfaces information such as when documentation was last updated and GitHub issue numbers.
The integration allows technical documentation to be kept up to date automatically, ensuring that the content is accessible and reducing the burden on developers to manually update docs.
Provides AI-ready infrastructure for enterprises, allowing teams to centralize, organize, and optimize code documentation.
Ensures that your codebase documentation is always accurate and up-to-date to support development processes.
Helps organizations build, manage, and maintain institutional knowledge across team members.
Utilizes expertise to analyze codebases, recognizing patterns to create a comprehensive understanding of the code.
Automatically captures and documents insights from Slack conversations or ad-hoc meetings to keep your knowledge base updated.
Ensures AI systems have the right context, avoiding misinformation or irrelevant data by linking documentation to the specific code.
Enables documentation to evolve with your code, supporting ongoing education and adaptation for teams.
Automatically documents entire codebases and creates detailed maps.
Keeps documentation in sync with code changes, ensuring accuracy.
Provides just-in-time documentation for developers, enhancing relevancy.
Integrated with both common industry and custom IDEs to support developers with real-time code awareness.
Create documentation that matches and maps developer’s cognitive flow.
Prevents repeated coding mistakes by enforcing doc patterns, guides, and standards.
Automatically synchronizes documentation with code updates to keep them aligned.
Allows you to integrate documentation directly into your IDE or web application for seamless access.
Supports multiple programming languages for more flexible documentation.
Keeps your code data local and secure by evaluating models on-premise or in your private cloud.
Ensures all documentation is checked and validated during continuous integration/continuous delivery processes.
Automatically generates flow diagrams from your codebase to provide visual aids.
Create, maintain, and discover all of your documentation in VS Code.
Integrate documentation into your CI & keep docs up to date automatically.
Create, maintain, and discover all of your documentation in JetBrains.
Turn GitHub Copilot Chat outputs into documentation.
Integrate documentation into your CI & keep docs up to date automatically.
Integrate documentation into your CI & keep docs up to date automatically.
Create, maintain, and discover all of your documentation in IntelliJ.
Create, maintain, and discover all of your documentation in PyCharm.
Create, maintain, and discover all of your documentation in WebStorm.
Create, maintain, and discover all of your documentation in Rider.
Create, maintain, and discover all of your documentation in PhpStorm.
Create, maintain, and discover all of your documentation in Android Studio.
Integrate documentation into your CI & keep docs up to date automatically.
Integrate Swimm documentation into your Backstage software catalog.
Get notified when documentation is created, viewed, or updated.
Create diagrams and charts that stay up to date with Swimm Smart Tokens.
Connect Swimm docs to components in Compass by Atlassian.
Powered by LinearB: Automate doc-related pull requests.
Swimm's platform automatically generates, updates, and maintains accurate, context-aware documentation in real time, integrating with the developer's workflow. It helps teams quickly understand databases and codebases, especially when dealing with legacy systems.
Uses deterministic static analysis to identify classes, functions, and logical components in the codebase to understand the structure and relationships.
Determines relevant context for specific topics or questions, ensuring precise documentation grounded in code context rather than AI guesses.
Incorporates large language models (LLMs) to generate structured, easy-to-understand documentation from code.
Tracks how often generated documentation is committed, serving as feedback on its accuracy and usefulness.
Measures user feedback to improve the system's output with ongoing feedback.
Facilitates feedback through "back to comments" features to ensure content gets revised, incorporated, and improved.
Includes Language Plugins to handle both modern and legacy programming languages effectively, with options for custom plugins for specific language and file formats.
Swimm for GitHub Copilot automates the documentation process by integrating with AI, providing documentation suggestions based on code context.
Users can start a conversation with GitHub Copilot chat to ask questions about the code, such as what the code does or how to use it.
Generates documentation automatically by suggesting content whenever new code is added or existing code is updated.
The extension can suggest and add documentation where it detects missing information, thereby ensuring code comprehension.
Tightly integrates documentation with the actual code, helping teams maintain up-to-date and relevant information.
Utilizes code analysis and machine learning to automatically generate documentation for legacy code which is often under-documented, aiding developers in understanding code bases quickly.
Built directly into the Swimm platform, it integrates with existing workflows to provide seamless and continuous documentation generation as code changes.
Provides detailed examples of how auto-docs look, including code snippets and explanations directly within the documentation for better clarity.
Focuses on providing developers with a comprehensive understanding of the existing codebase to facilitate more efficient refactoring.
Advocates for making small, manageable changes over time rather than large overhauls to improve stability and maintainability.
Encourages teams to simultaneously address existing technical debt while implementing new features.
Recognizes that not all legacy code should be treated equally and emphasizes assessing the need and value of refactoring different parts of the code.
Promotes a mindset within teams to continuously improve and refactor code as an ongoing practice to maintain code quality and reduce technical debt.