AI Architect
AI that understands your codebase inside out — and codes like your team.
Bito’s AI Architect builds a knowledge graph of your entire codebase.
It analyzes all your repositories (whether you have 50 or 5000 repos) to learn about your codebase architecture, microservices, modules, API endpoints, design patterns, and more.
AI Architect can then provide accurate and well-written code.
This fundamentally changes the game for enterprises with many microservices or large, complex codebases.
We believe AI Architect provides an ability to perform a code review similar to how a principal engineer or an architect would be able to help connect the dots on the impact of your changes across your codebase.
How you can use AI Architect
AI Architect is designed to be flexible and can power multiple use cases across different AI coding tools and workflows.
Integrate via MCP server – Use AI Architect as an MCP (Model Context Protocol) server to connect with tools like Claude Code, Cursor, Windsurf, and GitHub Copilot (VS Code). It helps connected tools understand your codebase and workflows better, resulting in accurate and more reliable suggestions.
Example: Bito’s AI Code Review Agent – One example of AI Architect in action is Bito’s AI Code Review Agent, which uses AI Architect to deliver smarter, context-aware code reviews directly in your pull requests and IDEs.
Why use AI Architect?
Most AI coding tools struggle with accuracy in real-world codebases because they don’t know your internal APIs, endpoints, or library usage.
AI Architect eliminates this problem by learning from your actual repositories.
How your team structures code
How different services interact
How external dependencies are used
Etc.
Key benefits include:
Accurate, production-ready code — because it knows your codebase inside out.
Consistent design adherence — aligns with your architecture patterns and coding conventions.
Faster onboarding — new engineers or AI agents can quickly understand the system structure.
Enhanced documentation and diagramming — through its internal understanding of interconnections between modules and APIs.
Smarter code reviews — reviews with system-wide awareness of dependencies and impacts.
How AI Architect differs from Embeddings?
Traditional embeddings work like a search engine — they retrieve code snippets or documents similar to a given query.
They can find related content but can’t understand how different parts of your system work together.
AI Architect, on the other hand:
Builds a knowledge graph that captures relationships between repositories, modules, APIs, and libraries.
Provides precise answers and implementations, not just search results.
Understands context and intent — how and why something is implemented in your codebase.
Enables system-aware reasoning, allowing AI agents to generate or review code with full architectural understanding.
Getting started
Contact Bito support team at [email protected] to have your repositories indexed. You will get an MCP server URL and Auth token from Bito.
Configure MCP server in supported AI coding tools such as Claude Code, Cursor, Windsurf, and GitHub Copilot (VS Code).
Select your AI coding tool from the options below and follow the step-by-step installation guide to seamlessly set up AI Architect.
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