Swimm delivers agentic application modernization for large codebases. It pairs a proprietary static analysis engine that maps dependencies, entry points, data flows, dead code and cross-repo relationships with AI agents that apply verified patterns at scale, and senior engineers who review the output. Offerings include API-first extraction of business logic, an agentic context layer that gives AI coding tools a validated knowledge base of your code, and large-scale migrations such as dotnet, Java and monolith to microservices. Work runs in a live customer workspace.
Swimm is built for enterprises with legacy systems that are hard to change, including banks and organizations running COBOL, JCL and PL/I on mainframes alongside modern languages. Engineering leaders and architects who need an accurate map of an application before touching it are a core audience, as are business analysts who need documented rules and flows. Security-sensitive teams can deploy on premises, including air-gapped environments, and bring their own LLMs. The model is an engagement, so it fits organizations ready to scope a modernization project rather than individuals browsing for self-serve software.
Engagements move through four stages, each at a fixed price: assessment, specification, modernization and enablement. A team might start with an assessment that snapshots the tech stack, risks and success criteria, then extract business rules from a mainframe application and hand them to analysts. Another might build a knowledge base that Cursor or internal agents query through MCP so changes are made safely. Delivered output can be migrated code, extracted services, shipped APIs, architecture and dependency maps, test plans, validation evidence and playbooks for the in-house team to keep evolving the system.
