D Dana Claude Code
“Can I change the payments API?”
4 consumersorders-service, ledger-sync, accounting, fraud-detection
The context your AI agents were missing.
Code Swan feeds your AI tools a live map of your whole system, including architecture, APIs, ownership, and blast radius, so every change is grounded in how your code really works.
- 3×
- more accurateon your own system
- −32%
- fewer tokenssame answer, less spend
- 5×
- fewer hallucinationsless rework for reviewers
Measured across four frontier models and 3,300 scored answers. Read the benchmark →
Every AI agent on your team, working from the same truth.
Different people, different tools, different questions — every answer comes from the same living catalog.
A Ari Cursor
“Do we already have rate limiting?”
throttle-mwpackages/throttle-mw · imported by 9 services
CIGitHub Actions · pull request #482
“What does this change affect?”
3 services · 2 teamscheckout-web, billing-api, ledger-sync
S Sam Copilot
“Who owns ledger-sync?”
@team-dataowner in the catalog since v2.41
N Nora Product manager
“Which teams does checkout touch?”
4 teamspayments, orders, fraud-risk, data-platform
M Maya Windsurf
“Who listens to order events?”
5 subscribersaccounting, fraud-detection, search-index +2
DocsConfluence · architecture page
“Is this page still accurate?”
12 components refreshedlast sync 4 minutes ago
ReleaseArgo CD · v2.5.0
“What moves in this release?”
6 servicespayments-api v1 → v2 · 4 consumers to notify
Everyone who asks — engineers, product, design, your CI, your docs — reads the same map.
Measured, not claimed
Your assistant is not guessing because it is weak.
It is guessing because nobody showed it the system.
We asked four AI models twenty questions about one system of sixty repositories. Which services call which. Who reads each event. What stops working if a service goes down. Then we marked 3,300 answers against a key we wrote by hand.
40 → 8
wrong services named, per 100 answers
3.9×
more of the answer per token read
3,300
marked answers, four models
Read the full report Why assistants miss on large codebases →
Across the SDLC
We're with you at every stage of your SDLC.
One system map, delivered via MCP, so every stage — and every role — works from the same always-current picture.
Plan 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.
Skills you can run here, among others:
/find-reuse/feasibility-check/what-powers
Build 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.
Skills you can run here, among others:
/explain-component/blast-radius/find-api
Review 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.
Skills you can run here, among others:
/review-with-context/find-pii/boundary-check
Test QA
Test the risk that actually exists
- End-to-end tests written against how a flow actually runs, call order, real endpoints, and which hops are events, not a topology diagram six months out of date.
- Know exactly which flows and consumers a change affects, so coverage targets real risk.
- Full API contract visibility, including contracts with no known consumers.
Skills you can run here, among others:
/test-plan/flow-map/contract-check
Ship 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.
Skills you can run here, among others:
/who-owns/blast-radius/review-with-context
Support 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.
Skills you can run here, among others:
/trace-feature/who-owns/component-docs
-
Plan for 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.
Skills you can run here, among others:
/find-reuse/feasibility-check/what-powers
-
Build for 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.
Skills you can run here, among others:
/explain-component/blast-radius/find-api
-
Review for 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.
Skills you can run here, among others:
/review-with-context/find-pii/boundary-check
-
Test for QA
Test the risk that actually exists
- End-to-end tests written against how a flow actually runs, call order, real endpoints, and which hops are events, not a topology diagram six months out of date.
- Know exactly which flows and consumers a change affects, so coverage targets real risk.
- Full API contract visibility, including contracts with no known consumers.
Skills you can run here, among others:
/test-plan/flow-map/contract-check
-
Ship for 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.
Skills you can run here, among others:
/who-owns/blast-radius/review-with-context
-
Support for 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.
Skills you can run here, among others:
/trace-feature/who-owns/component-docs
Just a few of the highlights, the possibilities are endless.
Living Software Catalog
Every service, API, and owner in one always-current catalog.
A queryable record of every component in your system, built directly from your codebase and aggregated across GitHub, GitLab, and Bitbucket. Nothing to write by hand. Nothing that drifts from reality.
Explore more →
Get started
From connect to context in 3 days.
Point Code Swan at your repos and watch your system become queryable by every agent on your team.
Book a demoDay 1
Connect your repos
Read-only access, five minutes of setup. The scan starts immediately.
Day 2
Your catalog builds itself
Services, APIs, owners and dependencies, mapped into a living graph.
Day 3
Your agents get context
Every AI tool on the team queries it via MCP — grounded answers, lower token spend.
Deployment
Prefer to keep everything in your own perimeter?
Deploy Code Swan on your own clusters, using your own LLM keys. Your source code and metadata never leave your network, the full product, in your environment, under your controls.
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.
Why this gets worse on large codebases →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.