We tested 20+ AI models to measure the cost-quality frontier.
Context engine
Context engineering for
large, complex codebases
AI Architect builds a living knowledge graph from your code, docs,
and decisions, so coding agents get it right the first time.
Available across coding agents & issue trackers
- Cursor
- Claude Code
- Codex
- GitHub Copilot
- 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
Case study 
AI Architect reads the same signals a senior engineer would, our tickets, docs, review comments, and plans the way someone who knows us would plan.
Case study
Bito surfaced three options with real tradeoffs. The hardest part of senior engineering, and it was in the first draft.
Case study
Backed by Eniac, NGP Capital, Vela Partners, and NextView Ventures.
We’re built with from around the world.
Frequently asked questions
A code context engine builds a living, structured map of your entire codebase, the services, APIs, dependencies, patterns, and past decisions, and serves it to coding agents on demand. Bito’s context engine, AI Architect, holds this as a knowledge graph across all your repositories, so agents reason from how your system is actually built instead of grepping files one at a time.
AI Architect hands each request the exact files, symbols, and dependencies it needs, pulled from the knowledge graph rather than local search. Agents connect through MCP in Cursor, Claude Code, and Codex, so they stop exploring the repo to rebuild context and get cross-repo work right the first time.
The graph maps more than source. It captures architectural patterns, implementation playbooks, service and dependency topology, blast radius, engineering health signals like ownership and churn, and decisions from Jira, Linear, Confluence, and Slack. That is why it grounds design, coding, and review from one shared source, not just code generation.
Embeddings retrieve text chunks that look similar. The knowledge graph encodes how your system connects, typed relationships across API calls, database links, and message queues, so a query returns every downstream consumer of a schema or the full blast radius of a change. It updates as your code and tickets change, with no manual re-indexing.
No. Your code is never stored and never used to train models. AI Architect is SOC 2 Type II certified and end-to-end encrypted.