DECISION MODEL GUIDE
Rule-based vs AI campaign optimization
Both approaches can assist campaign teams, but they make different demands on evidence, explainability, training data, controls, and product claims.
In short
Rule-based optimization applies conditions defined by the team. AI optimization infers recommendations from a model. ArbiNeuro currently emphasizes explicit rules and explainable, user-controlled recommendations rather than claiming a fully autonomous AI.OPERATING MODEL
Two approaches — different levels of explainability
Explicit rules
✓Visible conditions
✓Predictable thresholds
✓User confirmation
Model inference
—Probabilistic output
—Needs model governance
—Requires separate validation
ArbiNeuro currently presents explainable rule logic and user-controlled recommendations — not a fully autonomous AI operator.
Where rules are strongest
Rules are useful when targets, limits, exceptions, and allowed actions can be stated clearly. The reason for a match can be inspected directly.
Where AI claims need proof
An AI feature should identify its input data, training or inference method, validation, limitations, confidence behavior, and human override before performance claims are trusted.
A practical control standard
Regardless of the decision model, the team should see affected scope, conflicts, permissions, provider capability, action result, and an audit trail.
ARBINEURO
Choose the decision model you can govern
Review one campaign rule and the evidence and controls it requires.
Book a demo ↗