The context layer your engineering
workflow is missing
Available across coding agents & issue trackers
- Cursor
- Claude Code
- Codex
- Jira
Trusted by teams at
AI agents accelerated coding. Technical design is still behind.
Senior engineers spend 60 to 70% of their time on work before coding starts. Feasibility analysis, technical design, cross-repo impact mapping, and epic breakdown. Every new feature creates that same queue at the same desks.
01Feasibility analysis
One engineer who knows the system
- Wrong things get built
02Technical design
Bottlenecked on system knowledge
- Weeks of rework downstream
03Cross impact analysis
Lack of full cross-repo visibility
- Surfaces in production
04Epic breakdowns
Knowledge leaves when people do
- Next sprint starts blind
AI Architect is built for exactly this work
AI Architect builds a knowledge graph of your codebase and operational history, mapping services, dependencies, and APIs across every repository. 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 codebase context and operational history to every phase of development. Pre-coding, coding, and code review all draw from the same knowledge graph.
Feasibility analysis
AI Architect reads every spec against your live codebase and flags what is buildable, what needs rethinking, and what risks exist.
Feasibility report with gaps, risks, and recommended approach.
Technical design
AI Architect drafts a technical design document grounded in your service topology, existing patterns, and operational history.
Technical design with implementation approaches, effort estimates, and flagged landmines.
Cross-repo impact assessment
AI Architect maps every service, API, and dependency a change will affect across all repositories.
Impact map with affected services, APIs, and risk signals.
Epic breakdown
AI Architect breaks every epic into Jira-ready stories with enough context for a developer to act without another meeting.
Story breakdown with implementation context per ticket, posted directly in Jira.
60-70%
Of an architect's week, in one session
Every engineer
Same system knowledge
Grounded code generation
One-shot production-ready code, grounded in your actual service patterns and APIs.
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.
39%
Higher task success
5-9x
Faster task completion
50%
Faster onboarding
AI Code Reviews
Get codebase-aware pull request reviews and cross-repo impact analysis in every PR. Catch bugs, issues, and downstream risk before they reach production.
89%
Faster PRs
34%
Fewer regressions
How Bito works
No code storage or model training
Your code stays yours. No code is stored.
No model is trained.
End-to-end data encryption
Runs on-prem or in the cloud. All data in transit and at rest is encrypted end-to-end.
Enterprise-ready
SOC 2 Type II certified. Built for teams with strict security and compliance requirements.
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 repositories, services, APIs, and dependencies connect and interact. Without it, AI coding agents work on isolated files with no understanding of system-level impact. With it, every technical decision, from feasibility analysis 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 and operational history, delivering that context through agent skills in Jira, via MCP in your AI coding agent, and across your Git provider 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 symbols, modules, APIs, and dependency flows. It also indexes your operational history from Jira, capturing past decisions, incident patterns, and system behavior. Both update dynamically as your code and tickets change, so the context your team works from is always current.
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.
