Code Swan

Code Swan provides deep codebase intelligence to your AI coding tools through the Model Context Protocol (MCP). By mapping every API, cloud resource, architecture boundary, and ownership assignment, it ensures engineering teams have AI that understands the actual system. It bridges the gap between raw code and infrastructure reality. Most AI coding tools work one file at a time. They can read what's in front of them, but they don't know how your services connect, what a change will break, or which team owns the code they're editing. The gap isn't intelligence — it's context. Code Swan closes it by statically analyzing your source across GitHub, GitLab, and Bitbucket to build an always-current, queryable model of your whole system, then serving that model to Cursor, Claude, GitHub Copilot, and any MCP-compatible tool. What it maps - API Surface Intelligence — Every REST, gRPC, GraphQL, and async API your services expose and consume, including who calls what and where a breaking change will cascade. - Cloud Resource Topology — Every database, queue, bucket, and event bus your code touches, across any provider — mapped from source, with no cloud credentials required. - Architecture Auto-Detection — Bounded contexts, service ownership, and domain boundaries extracted from real code, with C4 diagrams regenerated on every scan. - Living Software Catalog — A single, self-maintaining record of every service, API, owner, and business capability. No entries to write. None that go stale. - Business Semantic Search — Ask questions by intent ("which service owns checkout?", "where is customer PII handled?") and get answers drawn from your actual system. - Blast Radius Analysis — Every dependency and consumer surfaced so your AI can reason about impact before a line ships. What makes it different Code Swan reads only your source code — no runti cloud access, no changes to your repos. Because it's derived from code, it's always current: you're grounded in your real system, not the wiki that hasn't been updated since Q2. Scanned once and cached centrally, so your wlayer instead of each tool burning tokens rediscovering the same system. What you can expect - Deploy with confidence — see blast radius before merge and turn "I hope nothing breaks" into predictable releases. - Onboard in hours, not weeks — new engineers anll system on day one, no tribal knowledgerequired. - Lower AI token costs — shared, cached context the codebase. - Trustworthy AI output — suggestions grounded in how your system actually works, not plausible guesses. Impacting all company personas: Here is a concise summary of the core impact for each persona: Development — High-Precision AI & Risk-Free Execution:** Gives engineers and AI agents full multi-repo context so code compiles on the first try, while making blast radius, code search, and onboarding instant and predictable. QA — Targeted, Topology-Driven Quality:** Replaces outdated manual scripts with production-accurate E2E tests generated from your real system map, ensuring coverage zeroes in on actual risk and hidden API contracts. Product — Fast Feasibility & Safe Prototyping:** Allows PMs to pressure-test ideas, detect duplicates, and "vibe-code" integrations against real-system context before wasting engineering hours or token budgets. Customer Success — Autonomous Technical Clarity:** Enables client-facing teams to self-serve system knowledge via plain-language search, instantly mapping customer issues to the right services and engineering teams without interrupting developers. Security — Credential-Free Attack Surface Control:** Maps PII, cloud infrastructure, and API contracts directly from source code with zero cloud credentials, automatically catching boundary violations at the PR stage. Management — Frictionless Scaling & Living Governance:** Eliminates organizational debt with a zero-maintenance system catalog, automated PR routing, and the guardrails needed to scale AI adoption safely across the company. Set up in under five minutes: add the hosted MCP server's URL and token to your AI tool's settings. No code changes, no agents to run.

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