Grounded coding
Deep system context your
coding agents need
AI Architect gives every coding agent the system context
it needs to get it right the first time.
Available via MCP in
- Cursor
- Claude
- Codex
task success 
token cost Trusted by teams at
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
Code your system can rely on
Context is everything in engineering. Coding agents today don’t have it. AI Architect pulls context from your code, docs, past decisions, and Slack threads, giving every coding agent what they need to act autonomously.
1-shot production-ready code
higher task success rate
Trace failures to root cause, fast
faster task completion
New engineers, up to speed in days
faster onboarding
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.
Frequently asked questions
Indexing retrieves text chunks based on similarity, which works well for finding relevant code but falls short on reasoning about how the system connects. AI Architect builds a typed, directional graph of your codebase, mapping services, dependencies, APIs, and architectural patterns across every repository. When a coding agent works on a cross-repo change, the graph tells it exactly which components are affected, which patterns to follow, and what constraints apply, which is reasoning that similarity search cannot produce.
AI Architect connects to any MCP-compatible coding agent. This includes Cursor, Claude Code, Windsurf, and Codex. Once connected via MCP, your coding agent draws from the same knowledge graph for every task.
AI Architect builds a connected knowledge graph across all your repositories, mapping services, APIs, dependencies, and architectural patterns. It also pulls from commits, issues, docs, and past decisions, capturing why the system was built the way it was, not just how it looks today. Both layers update dynamically as your code and conversations change. On SWE-Bench Pro, coding agents grounded in this context moved from 51.9% to 70.1% task success on large, multi-file changes, while cutting token cost per task by 47%, independently evaluated by The Context Lab.
No. AI Architect lowers the cost. Coding agents without codebase context spend most of their token budget on navigation, reading hundreds of files to locate where the change belongs. With AI Architect, the agent consults the knowledge graph once, then reads only the files relevant to the change. On SWE-Bench Pro, this cut token cost per task by 47%, with reductions up to 68% on the heaviest engineering work. The same agent, the same task, half token cost.
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.