The context layer your engineering
workflow is missing
Available across coding agents & issue trackers
- Cursor
- Claude Code
- Codex
- Jira
Trusted by teams at
Context exists everywhere in your stack.
Most of it stays locked in silos.
Technical decisions wait on the two engineers who actually know the system.
Coding agents build without knowing how your services connect.
Code reviews analyze the diff. Downstream risk surfaces in production.
AI Architect is built for exactly this work
AI Architect builds a knowledge graph of your codebase, operational history, and business context, capturing technical decisions, engineering patterns, and tribal knowledge across your entire system. It then runs the feasibility analysis, technical design, and cross-repo impact assessment your team either skips or spends days on.
Spec to PR. Every step informed by your actual codebase.
AI Architect brings the same system context to every phase of development. Design and scoping, grounded coding, and code review all draw from the same knowledge graph.
Feasibility analysis
Flags what is buildable, what needs rethinking, and where risks need investigation before the team commits.
Technical design
Drafts a technical design document grounded in your service topology, existing patterns, and past decisions.
Impact assessment
Maps every service, API, and dependency a change will affect across all repositories.
Scope breakdown
Breaks every epic into Jira-ready stories with effort estimates and enough context for a developer to act.
Jira, 
Linear, 
Slack60-70%
Of an architect's week, in one session
Days → Hours
To decide what to build
Grounded code generation
One-shot production-ready code, grounded in your actual service patterns and APIs, and dependencies across all repositories.
Accelerated onboarding
New engineers ask system-level questions in their coding agent. AI Architect answers from the live knowledge graph.
Production issue triage
Trace failures through your service topology. Surface root cause without hours of manual investigation.
Cursor, 
Claude Code, 
Codex39%
Higher task success
5-9x
Faster task completion
50%
Faster onboarding
AI Code Reviews
AI Architect-powered pull request reviews and cross-repo impact analysis in every PR. Catch bugs, issues, and downstream risk before they reach production.
GitHub, 
GitLab, 
Bitbucket89%
Faster PRs
34%
Fewer regressions
Built for enterprise
No code storage or model training
Your code stays yours. No code is stored. No model is trained.
Flexible deployment
Deploy on-prem or in Bito cloud, your choice.
Security and compliance
SOC 2 Type II certified. End-to-end encrypted.
From engineering teams
AI Architect changed how we start every feature. Our engineers come to planning with a grounded technical design already in hand, not a blank page.
Teams felt a difference immediately and started saving 30% to 35% of human hours spent in code review each week.
Backed by Eniac, NGP Capital, Vela Partners, and NextView Ventures.
We’re built with from around the world.
Frequently asked questions
A context layer is a live, structured understanding of your entire engineering system, how your services, APIs, dependencies, and decisions connect and interact. Without it, agents work on isolated files and engineers make decisions from memory. With it, every phase of development, from design to code generation to code review, is grounded in how your system actually works.
AI Architect is the context layer for your engineering workflow. It builds a knowledge graph of your codebase, issue trackers, docs, and operational history, and delivers that context through agent skills across every phase of development. In Jira and Linear for technical design and scoping. Via MCP in your coding agent for grounded code generation. Across GitHub, GitLab, and Bitbucket for codebase-aware code reviews.
Most code review tools analyze only the files in the diff. Bito’s AI Code Reviews use AI Architect’s knowledge graph to review every pull request in the context of your full system. That means cross-repo impact analysis, dependency awareness, and blast radius detection, catching issues before they merge rather than after they surface in production.
AI Architect builds a connected knowledge graph across all your repositories, mapping services, APIs, dependencies, and architectural patterns. It also indexes your operational history from Jira and Linear, capturing past decisions, incident patterns, and feature rationale, and pulls architecture decisions and business context from Confluence. Both update dynamically as your code and tickets change, so every agent and engineer always works from current context.
Bito supports cloud and on-prem deployment. Your code is never stored and never used to train models. Bito is SOC 2 Type II certified and designed for enterprise grade security and compliance.
