Code Swan

Here's a cost nobody puts on a dashboard: every time someone opens Cursor, Claude, or Copilot and asks about your system, the tool starts from zero. It crawls files, greps around, follows imports, and burns tokens reconstructing how your services fit together — just to answer one question. The next engineer opens a different tool, and it does the whole thing again. Even teams who've done everything right pay this tax. Say your docs and context files are genuinely up to date — great. Your AI still reads all of them into a fresh session, from scratch, every time. Perfect docs don't save the token bill; you just pay to re-load the right answer instead of reconstructing it. Code Swan scans your system once. It statically analyzes your code across GitHub, GitLab & Bitbucket, builds an always-current map — services, APIs, events, resources, owners, dependencies — and caches it centrally, indexed so a tool can query the exact slice it needs. Then it serves that map to every AI tool through one MCP server. What that changes: - Scan once, share everywhere — built a single time, reused by Cursor, Claude, Copilot, and any MCP tool - Query, don't dump — indexed data means the AI pulls the one edge or owner it needs, not a wall of docs into context - Lower token spend — answers handed over, not re-crawled or re-loaded every session - Faster + more accurate — no crawl step, and a shared static map beats each tool's partial guess - Always current — refreshed on scan, so "shared" never means "stale" Reads source only — no runtime access, no repo changes. Setup under 5 min: drop the MCP URL + token into your tool's settings. How many AI tools are crawling your codebase right now — and do you know what that's costing you?

We hear the same thing from teams of every size: "This is exactly what we need — but our source code cannot go to a third party. Not for scanning, not for metadata, not ever." For plenty of companies, where the code lives isn't a preference. It's a hard line, written into a contract, a policy, or a regulation. So we built the answer: Code Swan now runs fully self-hosted, inside your own perimeter. Same living catalog. Same architecture graph, API intelligence, cloud topology, semantic search, and MCP server your team already wants. Deployed on your clusters, in your cloud account, under your controls — not ours. How it stays inside your network: - Your clusters, your control — runs entirely in your own infrastructure, with no outbound connection to us and nothing leaving your perimeter - Bring your own model — scanning runs on your LLM keys and your existing provider contract, so only the provider you already trust ever sees your code - Source never leaves — repos are scanned in place, only metadata is built, and no repository data is sent to Code Swan - Your identity, your audit boundary — authenticates through your own SSO / SAML, keeping everything in your region and under your controls - The full product, nothing held back — self-hosted is a deployment choice, not a stripped-down tier Why it matters: teams whose code legally can't move now get the same AI-ready map of their whole system — API edges, async event flows, resource topology, ownership, C4 diagrams — grounded in their real system, without a single line of source crossing the boundary. Three steps, all in your environment: deploy to your cluster → connect your repos read-only → point it at your LLM keys. We work with your platform team to stand it up.

Every team has one: the Confluence page with the boxes and arrows, drawn in a workshop 8 months ago. It was accurate for about a week. Then a service got split, an API got deprecated, a new queue showed up, someone left the company — and nobody redrew it. Now it's a museum piece everyone quietly ignores, and the "real" architecture only lives in five people's heads. Your AI inherits that problem. Point it at a stale diagram or a README from last year and it confidently plans against a system that no longer exists. Code Swan kills the drift. It reads your source across GitHub, GitLab & Bitbucket and rebuilds the map on every scan — services, APIs, events, resources, owners, and C4 diagrams — so the picture is generated from the code that actually shipped, not the code someone remembered in a meeting. No diagram to maintain. No doc to update. It's just always right. What you get: - Living architecture — C4 diagrams regenerated from source on every scan, never hand-drawn, never stale - Real ownership — who owns each service and bounded context, straight from the code, not a spreadsheet - Complete dependency map — sync API edges and async pub/sub edges, tagged with the topic they travel on - Full resource topology — every database, bucket, queue & secret each service touches, across any cloud - True blast radius — what a change breaks, surfaced before merge, not discovered at 2am Why it's different: reads source only — no runtime access, no repo changes. Scanned once, cached centrally, and served to Cursor, Claude, Copilot, and any MCP tool — so every assistant plans against the same current truth instead of a guess. Setup takes under 5 minutes: drop the hosted MCP server's URL + token into your AI tool's settings. When did you last trust your own architecture diagram?

You don't ask "what's in the payments repo?" You ask "how does checkout work?" And checkout isn't a repo. It's a feature that lives across six services, three databases, and a couple of async events nobody remembers wiring up. Real context isn't a folder. It's a domain, and the dataflow running through it. That's the part that breaks you: a message published here, consumed three services away, feeding a flow you didn't know you were part of. Your AI can't see it. Most catalogs can't either. They stop at the service boundary. Code Swan maps the whole flow. Not just "service A calls service B," but the feature it belongs to, the domain it serves, and every hop the data takes, including the async messages between them. So when your AI reasons about a change, it sees the feature, not just the file. That's the difference between an assistant that guesses and one that actually understands your system. What's a feature at your company that secretly spans way more services than anyone admits?

You need it when you're planning ("does this already exist?"), when you're reviewing a PR ("what does this actually touch?"), when you're testing ("what's the blast radius?"), when you're deploying ("what breaks downstream?"), and on day one onboarding ("who owns this and how does it connect?"). Same question at every step: how does my system really fit together? And your AI is guessing at all of them. That's the old way. The new, smarter way to run software development is one source of truth that every AI tool can tap into, at every stage. That's why we built Code Swan as one MCP server, not five plugins. Connect it once, and the same always-current map of your whole system (services, APIs, dependencies, owners) flows into Cursor, Claude, and Copilot across the entire SDLC. Plan, code, review, test, ship. Same source of truth, wherever you're working. One integration. Under 5 minutes. No docs to maintain, nothing that goes stale. Just a modern, smarter way to build software. Which SDLC step does your AI help least with today?

Synchronous calls are the easy part. When service A calls service B's REST endpoint, it's right there in the code. But the dependencies that cause 2am incidents are the async ones — a service publishes an event to a queue, and somewhere else, something you've never heard of is subscribed to it. Your AI coding assistant can't see that edge. Neither can most catalogs. That invisible coupling is exactly where "this one-line change is totally safe" turns into a cascading outage. Code Swan maps it — statically, from source. We don't just resolve who-calls-whose-API. We trace publisher → subscriber over the actual queue or topic, and put the topic name right on the edge — so your AI knows a change to a publisher will land on every subscriber, before it ships. No runtime tracing, no cloud credentials. Just your code. What it maps: - Sync API edges — every REST, gRPC & GraphQL call, matched to the provider that serves it - Async event flows — every pub/sub edge, tagged with the topic/queue it travels on - Resource topology — every database, bucket, queue & secret each service touches, across any provider - Architecture & ownership — bounded contexts and owning teams, with C4 diagrams on every scan - Full blast radius — sync and async dependents surfaced together, so impact analysis is complete, not half of it Why it's different: it reads only your source across GitHub, GitLab & Bitbucket — no runtime access, no repo changes. Always current, scanned once and cached centrally, then served to Cursor, Claude, Copilot, and any MCP tool — so they stop burning tokens rediscovering the same system. Setup takes under 5 minutes: drop the hosted MCP server's URL + token into your AI tool's settings. 👉 https://peerpush.com/p/code-swan What's the worst async dependency that's ever bitten you?

Code Swan statically analyzes your code across GitHub, GitLab & Bitbucket to build an always-current map of every API, cloud resource, owner, and dependency and then serves it to Cursor, Claude, Copilot, and any MCP tool. The result: AI that knows how your services actually connect and what a change will break before it ships. Why it matters: - Trustworthy AI output: grounded in your real system, not plausible guesses - See blast radius before merge: turn "I hope nothing breaks" into predictable releases - Onboard engineers in hours, not weeks: no tribal knowledge required - Lower token costs: context is scanned once and shared, not rediscovered by every tool ✅ Reads source only — no runtime access, no repo changes ✅ Always current, cached centrally ✅ Setup in under 5 min — just add the MCP URL + token Would love your feedback! 🙌