Rules
Learns from your approvals — never the same fix twice.
What you decide once becomes a rule the system keeps to.
Learning guardrails let the good through and stop what doesn't fit.
The need
You want the system to improve instead of repeating the same mistakes.
How it works
01
Collect
Every approval, rejection and edit is captured as feedback.
02
Derive
Rules emerge: avoid, prefer or modify.
03
Apply
Rules act on content and decisions — visible and editable.
What's inside
Agent feedback (approved / rejected / edited)
Learned rules with conditions
Types: avoid, prefer, modify
Manual or learned (with sample size & accuracy)
Act on content and decisions
Old world → New world
The old way — and the way with GRVITY.
Old worldThe same correction every single day.
New worldEvery approval becomes a rule that sticks.
Old worldGuardrails exist only in someone's head.
New worldAvoid, prefer, modify — explicit and editable.
Old worldThe AI makes the same mistake twice.
New worldLearned rules act on content and decisions.
In daily use
When it kicks in — concretely.
When
You keep editing the same tone.
↓ GRVITY
A rule forms — future texts honor it.
When
VIPs shouldn't see discounts.
↓ GRVITY
The rule prevents it reliably, visibly documented.
What you're wondering
Aren't these just if/else rules?
Rules set the limits — the decision within them GRVITY makes by scoring. That scales across thousands of combinations without you coding every case.
Do the rules really learn?
They adapt to what works — within the hard limits you set. You keep the guardrails, GRVITY optimizes in between.
In the Hybrid Spectrum
Makes the hybrid and agentic levels better over time.
Control & safety
Rules are visible and editable — no black box.
Goes well with
See GRVITY work on your data.
In a short demo we walk the path from signal to action — on your setup.