The context layer you need
for autonomous development
Available across coding agents & issue trackers
- Cursor
- Claude Code
- Codex
- Jira
- Slack
Trusted by teams at
Context exists everywhere in your stack.
Most of it stays locked in silos.
Planning 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.
Scattered engineering context, one structured graph
AI Architect builds a living knowledge graph from your code, commits, issues, and docs, mapping the services, APIs, dependencies, and architectural patterns across all your repositories.
Spec to PR. Every step grounded in your system context.
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
AI Architect tops SWE-Bench Pro
Deep codebase context lifts coding agent task success by 35% and cuts token cost by 47% on large, real-world codebases.
Claude Opus 4.6
Without context
with codebase context
Build at half the token cost
On SWE-Bench Pro, the same agent given codebase context via AI Architect ships the same task at a fraction of the token cost.
token cost per task
reasoning steps per task
tool calls per task
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
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
Coding agents can attempt technical design, but they work from what is in the context window, not your full system. They miss cross-repo dependencies, existing patterns, past architectural decisions, and business context that live outside the files you share with them. AI Architect builds a knowledge graph across your entire system first, so the technical design it produces is grounded in how your system actually works, not just the files visible in the session.
AI Architect indexes your repositories, commit history, Jira and Linear tickets, Confluence docs, and observability data into a live knowledge graph. It maps services, dependencies, APIs, architectural patterns, past decisions, and business context across your entire system. The graph updates dynamically as your code and tickets change.
Yes. AI Architect triggers automatically when an epic or story is created in Jira or Linear, posting the planning and design output directly into the ticket. No new tools, no workflow changes required. Teams can also trigger it on demand via a comment or label in the ticket.
No. Your code is never stored and never used to train models. AI Architect is SOC 2 Type II certified and supports both cloud and on-prem deployment.
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.
