API Surface Intelligence
Every API exposed and consumed across your codebase, mapped automatically. See exactly who calls what, where breaking changes cascade, and which contracts have no known consumers.
Full deep dive →The Engineering Intelligence Platform
Context Changes
Everything.
Code Swan builds a complete, always-current map of your entire codebase and delivers it directly into every AI tool your team uses. Product architecture, domain boundaries, dataflows, APIs, service ownership, legacy code. All of it. When your tools actually know your system, your engineers move faster, rewrites stop stalling, and every engineer on your team has the context to move forward independently.
AI That Finally Knows Your System
Code Swan feeds your AI tools a live, complete map of your entire codebase via MCP. So instead of guessing at your system, they actually know it.
End Tribal Knowledge
The context locked in your senior engineers' heads gets extracted from code and made available to everyone. No more chasing down the one person who knows a service.
Legacy Code Is No Longer a Mystery
Know what old code does, what depends on it, and what breaks before you touch it. Your team can finally work with legacy code confidently instead of avoiding it.
Built-In Engineering Portal
Code Swan ships a web app with a detailed, automatically maintained catalog of every service, feature, and domain in your system. One place for engineers to explore the full codebase. Also queryable via GraphQL for teams that want to build on top of it.
Capabilities
Everything your team and your AI need to know.
Mapped automatically. Delivered via MCP.
One scan gives you a complete, always-current picture of your entire system. APIs, cloud assets, dependencies, blast radius, and more.
All of this lives in your AI's context via our MCP server →Cloud Resource Topology
Code Swan scans your source code to map every cloud resource your services connect to, databases, queues, storage, event buses, and more, across any provider, no credentials required. Your AI agents get the full picture of how your code reaches into the cloud.
Full deep dive →Architecture Auto-Detection
Bounded contexts, service ownership, and team domain boundaries extracted from real code, not the wiki page that hasn't been updated since Q2.
Full deep dive →Business Semantic Vector Search
Code Swan's vector search is available directly to your AI agents via MCP, letting them answer business-intent questions like "which service owns the checkout flow?" or "where is customer PII handled?" across your entire mapped codebase.
Full deep dive →Living Software Catalog
A queryable, always-current catalog of every service, API, owner, and capability, built live from your code. Zero maintenance overhead, zero staleness.
Full deep dive →Also included
Blast Radius Analysis
Code Swan maps every service dependency, API consumer, and cloud resource connection into your AI tools via MCP, so your agents can reason about what a change will affect and answer impact questions before anything ships.
Feature & Domain Correlation
Connect every service and API to the product capabilities it powers. When a stakeholder asks "what delivers checkout?", answer in seconds, not hours.
Dependency Risk Scoring
Surface high-coupling hotspots, orphaned services, and fragile dependency chains before they become incidents. Fix systemic risk on your schedule, not at 2am.
Automated E2E Test Generation
Production-accurate end-to-end tests generated from your real system topology, not handwritten scripts that lag six months behind your architecture.
Questions & Answers
AI Coding Assistants and Codebase Intelligence
Common questions about context, MCP, and how Code Swan improves the accuracy of AI-assisted engineering.
Why do AI coding assistants give incorrect or outdated suggestions?
AI coding assistants generate suggestions from their training data and the currently open file. They have no knowledge of your system's actual API surface, service dependencies, cloud resources, or architectural patterns, so they recommend deprecated APIs, miss cross-service dependencies, and duplicate code that already exists elsewhere in your codebase. The gap is not intelligence; it is context.
What codebase context does an AI coding assistant need to be accurate?
An AI coding assistant needs structured knowledge of your system's APIs, which endpoints exist and who calls them, service dependencies, cloud resource connections, domain ownership, and architectural patterns. With this context, it gives suggestions that are correct for your specific system, not just syntactically valid in isolation.
What is MCP and how does it improve AI coding tools?
MCP, the Model Context Protocol, is an open standard that connects AI coding assistants to external data sources. When connected to a codebase intelligence server, an MCP integration gives tools like Cursor, Claude, and GitHub Copilot structured knowledge about your system's APIs, architecture, and ownership, turning generic suggestions into system-aware ones.
About Code Swan's MCP server →How does Code Swan deliver codebase intelligence to AI coding tools?
Code Swan scans your source code to build a complete map of your APIs, cloud resource connections, service architecture, and domain ownership. This intelligence is exposed through a cloud-hosted MCP server. Engineers add the MCP server URL to their AI tool's settings, no code changes required, and their assistant immediately has full codebase context.
See how it works →What is an engineering intelligence platform?
An engineering intelligence platform automatically extracts and maps the structural characteristics of a software codebase, its APIs, service dependencies, cloud resource connections, and domain boundaries, and makes that intelligence available to engineering teams and their AI tools. It replaces manual documentation and outdated architecture diagrams with a live, queryable understanding of what the system actually does.
API Intelligence deep dive →Let's Talk
Tell us about your codebase and we'll show you what Code Swan can uncover.