Infrastructure on Autopilot
AI that monitors, manages, and acts on your behalf. Not a chatbot — an agent that uses your tools, visibly.
What We Believe
Most AI assistants are chatbots pretending to be helpful. You ask them to do something, they say "Sure!", then disappear into a black box. Maybe it worked. Maybe it didn't. You won't know until something breaks.
We think that's backwards.
AI should be more transparent than clicking buttons yourself — not less. When you tell Caveman to deploy your service, you should see exactly what it's doing: which form it's filling, what values it chose, where it paused because it needs your input.
No Magic
Everything AI does is visible. No hidden API calls, no secret actions. You see the same forms AI fills.
Same Code Path
AI uses the exact same UI you do. Same validation, same permissions, same everything. No backdoors.
User Learns
Watch AI do it once, do it yourself next time. AI teaches you the system by using it in front of you.
Transparency Over Magic
You're not trusting a black box. You're watching an expert use your tools — and learning how to do it yourself.
Three Ways AI Helps
AI shows plan, fills forms, you confirm key steps
deploy gyr to fly
AI suggests action, you decide what to do
Memory 40% higher than normal
AI acts automatically, you get notified
CPU > 90% for 10 min
You control how much autonomy AI has. Reactive requires your confirmation for everything. Advisory shows alerts but waits. Autonomous follows rules you pre-approve.
The Action Bar
One input does everything: search, commands, natural language. No mode switching. History shows actions, not conversation.
CPU at 98% on api-gateway for 3h
Scale 3→5? Or write better code
Auto-scaled api-gateway 3→5
Rule: "High Load" triggered • 5m ago
Deployed gyr v2.1.3 → fly/iad
15m ago
Warning: Memory creeping up on ml-inference
Restarted automatically • 1h ago
Ghost Mode
When AI performs a complex action, it shows each step in real-time. Like watching an expert fill out forms — except you can intervene at any point.
Automation Rules
Rules are pre-approved workflows. When conditions match, AI acts automatically — no confirmation needed, because you already approved it.
Scale Up — High Load
If job queue > 1,000 → Scale to 5 machines
Scale Down — Low Traffic
If job queue < 3 → Scale to 1 machine
Hot Potato
If CPU > 90% for 10min → Add 2 cores
AI Monitoring
AI continuously watches your services and sees what dashboards can't: patterns, anomalies, correlations, predictions.
Memory 40% higher than normal
Possible leak in api-prod since deploy #847
Latency spike correlates with deploy
Response time increased 3× after deploy #847
You'll hit memory limits in 3 days
Based on current growth rate
staging-worker is idle
4 requests in 7 days, costing $23/month
Key Principles
One input does everything — no mode switching
No chat bubbles — show action cards, not messages
AI is invisible until it has something to say
History = actions performed, not conversation
Ghost Mode — AI shows its work, user can intervene
Rules = pre-approved workflows with constraints
Transparency > Magic — users learn by watching
The bottom line: AI should make you better at managing your infrastructure, not dependent on it. When you watch Caveman work, you're learning. When you set up rules, you're teaching. It's a partnership, not a black box.