Swimm

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.

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.

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.

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.

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.

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).

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.

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.

Analyzes source code across multiple programming languages (COBOL explicitly supported; Java, Python, C/C++ assumed) using language-specific parsing and analysis rules. Extracts business logic, control flow, and dependencies using syntax and semantic understanding tailored to each language's paradigms (e.g., COBOL's procedural structure, Java's OOP patterns, Python's dynamic typing). Handles polyglot systems where multiple languages interact, though accuracy may degrade at language boundaries.

Supports deployment in on-premise and air-gapped (disconnected) environments where code never leaves the customer's infrastructure. Enables analysis of sensitive codebases (financial systems, healthcare, government) without sending code to cloud services. Supports customer-managed LLM instances (Azure OpenAI, OpenAI Enterprise, self-hosted models) so that both code analysis and AI context provision remain within customer control. Complies with SOC 2 Type II and ISO 27001 standards.

Exports auto-generated documentation in standard Markdown format (`sw.md` files) that can be read, edited, and rendered in any Markdown-compatible tool (GitHub, GitLab, VSCode, Notion, Confluence, etc.). Documentation is stored in Git repositories as first-class artifacts, enabling version control, branching, and merging alongside code. Provides portability and reduces vendor lock-in by using open standards, though Swimm-specific metadata sections (Smart Tokens, analysis metadata) may not render correctly in other tools.