The Engineering Intelligence Platform

Deep codebase understanding delivers the ultimate context for
your AI agents.

Code Swan feeds your AI tools a live map of your whole system: architecture, APIs, ownership, blast radius, so every change is grounded in how your code really works.

  • AI that actually knows your system A live, complete map of your codebase, delivered to your tools via MCP.
  • End tribal knowledge & legacy mysteries See what old code does, what depends on it, and what breaks before you touch it.
  • Cut AI token costs across your team Scanned once and cached centrally, so no one burns tokens rediscovering context.
  • Built-in engineering portal An always-current catalog of every service, feature, and domain, queryable via GraphQL.
  • Empower non-engineer personnel Product, support, and ops teams can vibe code integrations, plan features, and run impact analysis, kept on the rails by full-system context.
Get in Touch See Capabilities
checkout-service · Code Swan MCP
✻ code-agent · connected to codeswan MCP
›
✦ Skill(work-in-context) · guided change workflow
● getComponent("payment-gateway") · codeswan MCP
⎿ service · TypeScript · criticality t1
● getGroupingMembers(component: "payment-gateway") · codeswan MCP
⎿ Payments feature · Commerce domain · role: owner
● getDependents("payment-gateway", rollUp: "grouping") · codeswan MCP
⎿ 2 features · 3 consumers
● getServiceGraph(scope: grouping("Payments")) · codeswan MCP
⎿ 5 nodes · 6 internal edges
One skill chains four MCP tools — grouping + graph — into a single guided loop before you touch a line.
›
✻ code-agent · connected to codeswan MCP
✦ work-in-context · brief
●
Context Payments feature · Commerce domain · owner · criticality t1
↑ consumes ledger-db, auth-service /verify, subscribes inventory.reserved
↓ produces POST /charge → Checkout, Subscriptions, publishes payment.settled → Notifications
You own a t1 component — preserve the /charge response shape; two downstream features parse it.
›
✻ code-agent · connected to codeswan MCP
✦ work-in-context · test plan
●
Checkout feature — /charge integration t1 · must test
Subscriptions feature — recurring billing flow t1
Notifications — payment.settled subscriber (event fanout)
⚠ crosses the Commerce domain contract — get Payments team sign-off
Ranked by blast radius and criticality — the test plan writes itself from the graph + groupings.
›
mock-customer-001
C1 — Context C2 — Containers C3 — Components
Containers & dependencies ▤ List view
◫ checkout-apiservice · TypeScript
W payments-workerworker · TypeScript
◫ payment-gatewayservice · TypeScript
W notifications-svcworker · Node
▤ ledger-dbdatastore · Postgres
W refunds-svcworker · TypeScript
uses exposes external

By Role

Value for every team it touches.

Code Swan maps your whole system once and delivers it via MCP, so every role works from the same complete, always-current picture.

Development

AI that fits your real system

  • Every prompt carries full multi-repo context, so agents ship end-to-end features that match your architecture and compile the first time.
  • Blast radius on demand: see every downstream consumer, dependency, and cloud connection a change touches before it ships.
  • Find code by meaning, not guesswork, and onboard against always-current architecture and ownership maps.

QA

Test the risk that actually exists

  • Production-accurate end-to-end tests generated from your real system topology, not scripts that lag six months behind.
  • Know exactly which flows and consumers a change affects, so coverage targets real risk.
  • Full API contract visibility, including contracts with no known consumers.

Product

Pressure-test ideas before a line is written

  • Check feasibility, downstream breakage, and duplicate APIs up front, killing flawed work before it burns eng hours or token budget.
  • See which services and APIs deliver each capability, answer "what powers checkout?" in seconds.
  • Vibe-code integrations and impact analysis kept on the rails by full-system context.

Customer Success

Scope issues without waiting on eng

  • Trace which services and capabilities a customer-facing feature depends on to set expectations fast.
  • Self-serve system understanding through plain-language semantic search.
  • Map customer-reported problems to the owning service and team.

Security

See the whole attack surface

  • Locate sensitive data, "where is customer PII handled?", across the entire mapped codebase.
  • Complete cloud topology mapped from source, no credentials or cloud API access required.
  • PR audits flag architecture and boundary violations before they reach production.

Management

A single, always-current source of truth

  • A living catalog of every service, API, owner, and capability across GitHub, GitLab, and Bitbucket, zero maintenance.
  • Safer AI adoption: guardrails let non-engineers contribute while keeping output aligned to your architecture.
  • Intelligent PR routing and dependency risk scoring make review load and systemic risk plannable.

Just a few of the highlights, the possibilities are endless.

Capabilities

Everything your team and your AI need to know.
Mapped automatically. Delivered via MCP.

Connect your GitHub, GitLab, and Bitbucket workspaces — multiple instances of each — and get one 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 →

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 →

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, aggregated from GitHub, GitLab, and Bitbucket into a single source of truth. Zero maintenance overhead, zero staleness.

Full deep dive →

Top Use Cases

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.

Guardrails for AI Coding

Full multi-repo context in every prompt via MCP, so agents generate end-to-end features that fit your architecture and compile on the first try. Product managers can pressure-test an idea for feasibility, downstream breakage, and duplicate APIs before a line is written, killing flawed work before it burns engineering hours or token budgets.

Intelligent PR Routing & Audits

Every pull request routed to the engineers who actually own the affected domains, with architecture and boundary violations flagged before they reach production. Cut review wait times and stop cross-boundary regressions at the door, not in the postmortem.

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 →

Which version control systems does Code Swan support?

Code Swan connects to GitHub, GitLab, and Bitbucket. You can connect multiple workspaces or organisations from each provider simultaneously, for example several GitHub organisations and a self-hosted GitLab instance at the same time. All repositories across every connected source are unified into a single observability layer, giving your team and their AI tools one consistent, accurate picture of the entire software landscape regardless of where the code lives.

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.