AI Governance for Engineering Teams
Define standards. Build with AI. Verify automatically.
SystemDox creates a closed loop where your architecture decisions are captured, enforced by AI agents, and continuously improved from failures. Humans stay in control at every step.
AI writes code fast. But does it write it right?
AI coding tools are powerful — but without your project context, they repeat the same mistakes, ignore your standards, and degrade your architecture over time.
Decisions stay in people's heads
Architecture choices happen in meetings. Writing them up takes hours nobody has. So they're never documented.
AI repeats the same violations
Without guardrails, AI tools ignore your auth patterns, use banned imports, and create the same cleanup work every sprint.
Manual review can't keep up
Code review catches some violations. But as AI output scales, humans can't review everything. Standards erode silently.
How it works
A closed loop that gets smarter over time
Each failure becomes a guardrail. Each guardrail prevents recurrence. Architecture quality ratchets up automatically.
Capture
Noteble
Publish
GitHub
Define
SystemDox
Build
AI Agents
Verify
Fitness Tests
Record decisions. AI writes the docs.
Use Noteble to capture architecture discussions via voice, photos, or text. AI generates structured documents — ADRs, specs, requirements — from one recording.
You review and edit every document before it's published. Nothing goes live without your approval.
From one recording
Guardrail
"Always use shared-observability wrapper, never import @sentry/react directly"
AI analyses the issue → generates fitness test automatically
Tech Stack
CLAUDE.md
Auto-generated project context for AI assistants
Teach AI your architecture
SystemDox indexes your documentation from GitHub repos. You define guardrails — plain-English rules with auto-generated fitness tests.
Report a common AI mistake. SystemDox analyses it, generates the bad pattern, correct approach, and a shell script that detects violations. You review and decide what to enforce.
Human decides what to enforcePlan features. AI implements them.
Describe what to build. SystemDox generates a plan with epics, issues, and acceptance criteria. You review and adjust before execution starts.
AI agents implement each issue — reading your ADRs, following your guardrails, and opening PRs. You review and merge every single one.
Execution Board
Fitness Tests — PR #42
✓ Uses SQS (per ADR-042)
✓ shared-observability wrapper
✓ Auth via resolve_principal()
✓ No direct Stripe imports
✗ Missing dead-letter queue config
PR blocked — developer reviews the failure
Automated checks. Human judgement.
Fitness tests run on every PR automatically — catching violations that manual review would miss. Cross-repo compliance matrix shows architecture health at a glance.
But humans decide what's a real issue vs. a false positive. You adjust the rules. You own the standards.
Every failure makes the system smarter
When patterns of failure emerge, report the issue in plain English. SystemDox analyses it, generates a guardrail with a fitness test, and injects it into every future AI session. The mistake never happens again.
AI keeps importing @sentry/react directly
Violation detected 3 times this week
You report it
"Should use shared-observability wrapper instead"
SystemDox generates
Guardrail + fitness test + AI context injection
Never happens again. Architecture improves.
AI proposes. Humans dispose.
AI accelerates
- • Drafts documents from recordings
- • Generates code from specs
- • Suggests guardrails from failures
- • Detects violations automatically
Humans decide
- • Review and approve every document
- • Merge or reject every PR
- • Choose which rules to enforce
- • Judge what's worth improving
AI does the 80% grunt work. Humans own the 20% that requires judgement.
Ready to close the loop?
Define your standards. Let AI build from them. Verify every PR automatically. Watch your architecture quality improve with every sprint.