Business Semantic Search
Find Code by Business Intent,
Not by Syntax
Engineering teams spend hours hunting for code that handles a specific business capability, asking colleagues, reading PRs, tracing logs. Code Swan's vector search lets your team and your AI agents ask business questions and get precise answers drawn from your real codebase.
The Problem: Business Context Is Invisible to Code Search
Standard code search finds text, a function name, a string, a file path. It cannot answer "what owns the subscription billing domain?" because no single file says that in plain language. The answer lives across dozens of services and functions, connected by business logic that only experienced engineers hold in their heads, and that AI agents guess at without it.
Questions Your Team and Agents Can Now Answer Instantly
These are representative queries that Code Swan's semantic search resolves against your real codebase.
Where do we handle payment failures?
Which services own customer PII?
What owns the checkout flow?
Where is rate limiting enforced?
Which service sends transactional emails?
Where do we validate user permissions?
Semantic Search as an MCP Tool, Inside Your AI Assistant
Code Swan exposes its vector search capability as a tool through its MCP server. AI coding assistants, Cursor, Claude, GitHub Copilot, and any MCP-compatible tool, can call this search directly when an engineer asks a business-level question.
AI agents are especially powerful when paired with vector search, they use it not just to answer questions, but to pull relevant code examples and patterns directly into their context when generating new code. When an agent needs to add authentication to a new service, it searches for how authentication is already implemented across your codebase and writes code that matches your actual conventions, not a generic example from training data.
How it works in practice
An engineer asks their AI agent to implement a new payment webhook handler. The agent searches the Code Swan catalog for existing webhook patterns in the codebase, finds three relevant implementations, and generates code that follows the same error handling, retry logic, and event structure already in use, consistent with the real system from the first line.
Frequently Asked Questions
What is business semantic search for a codebase?
Business semantic search is the ability to query a codebase using business language rather than code syntax. Instead of searching for a class name or a string literal, an engineer can ask 'where do we handle refunds?' and receive precise, contextually relevant results drawn from the system's actual code and architecture.
How is semantic search different from a regular code search or grep?
Traditional code search matches text, a function name, a string, a file path. Semantic search matches meaning. A query for 'payment failure handling' will surface relevant code even if no file uses those exact words, because the search understands business intent and maps it to the underlying code structure that Code Swan has already catalogued.
How do AI agents use Code Swan's semantic search via MCP?
Code Swan exposes a vector search tool through its MCP server. AI agents use it in two ways: to answer business-level questions about the system, and to find relevant code examples and patterns to reference when generating new code. When writing code that needs to follow an existing convention, an API call pattern, an error handling approach, an event structure, the agent searches for real implementations in your codebase and generates code that matches them, not a generic pattern from training data.
What does Code Swan's semantic search actually search?
Code Swan builds a structured catalog of your codebase, services, APIs, capabilities, ownership, and domain boundaries, derived from static analysis. The semantic search indexes this catalog using vector embeddings, allowing natural-language queries to match relevant entries across your entire system.
Let Your Team Ask Any Business Question About Your Codebase
Code Swan's semantic search turns months of undocumented tribal knowledge into an answer your team, or their AI tools, can retrieve in seconds.