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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Convert text-based diagram syntax into visual diagrams without using a GUI. Engineers write diagram definitions in a code-like language that renders as flowcharts, architecture diagrams, sequence diagrams, and other technical visualizations.
Embed and update diagrams directly within GitHub repositories, allowing diagrams to be referenced, versioned, and modified alongside code without leaving the GitHub interface or IDE.
Manage who can view, edit, and share diagrams within a team, with granular permission controls for different team members.
Search across all diagrams and documentation to find specific diagrams, components, or concepts within the team's documentation repository.
Add comments and annotations to diagrams for discussion, clarification, and collaborative feedback without modifying the diagram itself.
Validate diagram code syntax in real-time and provide error messages to help users fix issues before rendering.
Write technical documentation in markdown format with embedded diagrams, creating cohesive living documentation that combines prose and visual explanations in a single editable format.
Track changes to diagrams over time through version control, enabling teams to see what changed between diagram versions and maintain a complete history of documentation evolution.
+6 more capabilities
Performs static code analysis using proprietary deterministic algorithms (not LLM-based inference) to extract business rules, decision logic, validations, and control flow from source code without executing it. Analyzes code structure to identify conditional branches, loops, data transformations, and policy enforcement points, then maps these to human-readable business concepts. Works across multiple programming languages including COBOL, Java, Python, and C/C++, handling legacy and modern codebases up to 100M+ lines of code.
Unique: Uses proprietary deterministic analysis (not LLM inference) to extract 100% of business rules and decision logic without summarization or approximation, explicitly designed to handle legacy COBOL and complex financial systems where accuracy is non-negotiable
vs alternatives: More accurate than LLM-based code summarization tools (Copilot, GitHub Copilot) for extracting deterministic business logic because it performs structural analysis rather than statistical inference, making it suitable for compliance-critical systems
Automatically generates documentation in Swimm's `sw.md` Markdown format from analyzed code, embedding code snippet references with 'Smart Tokens' (superscript markers) that maintain bidirectional links to source code. Documentation is stored in the Git repository alongside code, enabling version control and automatic synchronization when code changes. CI/CD integration detects when documentation becomes stale relative to source code and flags it for review, ensuring documentation freshness without manual maintenance.
Unique: Stores documentation in Git alongside code with bidirectional Smart Token links, enabling version control and CI-based freshness checks that prevent stale documentation from being merged — a doc-as-code approach that treats documentation as a first-class artifact
vs alternatives: Superior to manual documentation and static doc generators because it maintains live links to code and enforces freshness via CI checks, preventing the documentation-code drift that plagues traditional approaches
Swimm scores higher at 55/100 vs Eraser at 48/100.
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Offers proof-of-concept (POC) programs and flexible project-based pricing for system integrators and enterprises evaluating Swimm. Sales-driven engagement model with custom quotes based on codebase size (lines of code), deployment model (cloud vs. on-premise), and LLM provider (Swimm-hosted vs. customer-managed). No public pricing available — requires contact with sales team for evaluation and pricing.
Unique: Offers flexible project-based pricing and POC programs tailored to enterprise needs, rather than standardized SaaS tiers — enabling custom engagement for large organizations with specific requirements
vs alternatives: More flexible than fixed-tier SaaS pricing for enterprise customers with custom requirements, but less transparent and more friction-heavy than self-serve tools like GitHub Copilot
Generates visual representations of user interface screens and workflows from legacy code analysis without requiring runtime execution. Extracts UI structure, field definitions, navigation flows, and screen transitions from source code (particularly effective for COBOL-based systems with embedded screen definitions), then renders these as diagrams and documentation. Enables non-technical stakeholders to understand system behavior and data flows through UI mockups derived purely from static code analysis.
Unique: Generates UI screens from static code analysis without runtime execution, specifically optimized for legacy COBOL systems where UI structure is explicitly defined in code — enabling modernization teams to understand system behavior without running decades-old systems
vs alternatives: More practical than runtime screen capture tools for air-gapped or offline legacy systems, and more accurate than manual documentation because it derives screens directly from code structure
Maps codebase structure to business functions and tracks data flows, dependencies, and system boundaries across programs, jobs, and subsystems. Creates a dependency graph showing how code modules interact, where data flows between systems, and which business functions depend on which code components. Enables architects and teams to understand system topology, identify integration points, and plan modernization or refactoring efforts with full visibility into cross-system dependencies.
Unique: Combines code analysis with business function mapping to create bidirectional links between technical code structure and business capabilities, enabling architects to reason about system topology at both technical and business levels simultaneously
vs alternatives: More comprehensive than static dependency analyzers (like Understand or Lattix) because it maps dependencies to business functions, not just code modules, making it more actionable for modernization planning
Exposes analyzed code understanding via the Model Context Protocol (MCP) standard, enabling AI agents and LLM-based tools to consume Swimm's code analysis as structured context. Provides deterministic code insights (business rules, dependencies, flows) to AI agents in a standardized format, allowing agents to make informed decisions during code modernization, refactoring, or generation tasks. Supports both Swimm-hosted LLMs and customer-managed LLM instances (Azure OpenAI, OpenAI Enterprise, or self-hosted models).
Unique: Bridges deterministic code analysis with agentic AI workflows via MCP, enabling AI agents to access accurate, non-hallucinated code understanding rather than relying on LLM inference — critical for code modernization where accuracy is non-negotiable
vs alternatives: More reliable than passing raw code to LLMs because it provides pre-analyzed business logic and dependencies via MCP, reducing hallucination and enabling agents to make better decisions during modernization
Integrates Swimm documentation directly into IDE environments (VSCode confirmed, others unknown) enabling developers to browse auto-generated documentation, view code-to-doc links, and edit documentation without leaving their editor. Renders `sw.md` files with Smart Token links that jump between documentation and source code, providing seamless navigation between understanding (docs) and implementation (code). Supports inline documentation viewing and editing within the development workflow.
Unique: Embeds bidirectional code-to-documentation navigation directly in VSCode via Smart Tokens, allowing developers to understand code without context switching — treating documentation as a first-class IDE artifact alongside code
vs alternatives: More convenient than external documentation tools (Confluence, Notion) because it keeps developers in their IDE and provides direct code links, reducing friction in the understand-code-read-docs workflow
Integrates with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.) to automatically detect when code changes make documentation stale and blocks merges or flags PRs until documentation is updated. Compares code changes against corresponding documentation to identify mismatches, then reports freshness status as a CI check that can be configured to block or warn. Prevents outdated documentation from being merged into the repository, enforcing documentation-as-code discipline.
Unique: Treats documentation freshness as a CI/CD quality gate, automatically detecting code-documentation mismatches and blocking merges until resolved — enforcing documentation discipline at the infrastructure level rather than relying on manual review
vs alternatives: More effective than manual code review for catching stale documentation because it's automated and consistent, preventing the common pattern where documentation lags code changes by weeks or months
+3 more capabilities