Bito provides the context layer your engineering workflow is missing. It works across the entire software development lifecycle, from planning in Jira to generating code in your coding agent, and across your Git provider for codebase-aware code reviews.
Bito's AI Architect builds a knowledge graph of your codebase (from repos to modules to APIs) and operational history from Jira (capturing past decisions, incident patterns, and system behavior). This gives your team a shared, always-current understanding of the system, and makes that understanding available wherever engineering decisions are made:
In issue trackers (Jira) for feasibility analysis, technical design, and cross-repo impact assessment.
(Claude Code, Cursor, Windsurf, GitHub Copilot, and more) for grounded code generation, accelerated onboarding, and production issue triage.
(GitHub, GitLab, Bitbucket) for codebase-aware AI code reviews.
Choose what you'd like to set up:
Technical design and planning — AI Architect analyzes your Jira tickets and posts a complete implementation plan directly in comments section: feasibility assessment, story breakdown, effort estimates, risk flags, and dependency mapping.
Grounded 1-shot production-ready code — The AI Architect learns all your services, endpoints, code usage examples, and architectural patterns. The agent automatically feeds those to your coding agent (Claude Code, Cursor, Codex, any MCP client) to provide it the necessary information to quickly and efficiently create production ready code.
If you have any questions, feel free to email us at
Self-hosted (Run AI Architect on your own infrastructure for maximum control)
Integrate with your tools:
Use in Git (GitHub, GitLab, Bitbucket)
(VS Code, Cursor, Windsurf, JetBrains)
(integrates seamlessly with AI coding agents like Cursor, Claude Code, Windsurf, and others.)
Consistent design adherence — Code generated aligns with your architecture patterns and coding conventions.
Spec-driven development — Automatically generate highly detailed, implementation-ready technical requirement documents (TRDs) and low-level designs (LLDs) with a deep, context-aware understanding of your codebase, services, and design patterns, ensuring architectural integrity and consistency at a granular level.
Triaging production issues — Easily and quickly find root causes to production issues based on errors/logs/etc.
Faster onboarding — New engineers or AI agents can quickly understand how a system or component 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.
Agent settings
