Glossary · Reasoning Layer · Retail AI
What is an AI Decision Engine?
A retailer's stack is good at two things: storing data and executing sends. The gap between them is the decision, the judgment about what should happen next for a specific customer. An AI Decision Engine fills that gap. It reads enriched customer data, reasons over it, and commits to a concrete next best action rather than handing a marketer another dashboard to interpret.
Replenit's AI Decision Engine is fine-tuned on TPU-accelerated hardware with retail-specific skills, so its decisions carry domain judgment, not just statistical correlation. Every decision it commits leaves with the reasoning attached, which is what makes the output inspectable, auditable, and safe to execute automatically.
The distinction
A decision engine reasons and commits a decision. A score, a router, and a rules engine do not.
A propensity score ranks likelihood but leaves the actual decision to a human. A routing agent moves messages between systems without deciding the move. A rules engine fires on static if-then logic a person wrote in advance. None of them reason about the individual customer and commit to the best next action.
An AI Decision Engine does exactly that. It weighs the customer's context, decides the next best commercial move, and attaches the rationale so the decision can be trusted and traced. It is the difference between a number on a chart and a decision ready to execute.
How it works
Raw events become execution-ready decisions. The engine is the stage where reasoning happens.
Every stage feeds the next: events are enriched, written to memory, reasoned over by the engine, and shipped by Maestro as execution-ready decisions into the systems you already run.
Side by side
Four things a stack can output for a customer. Only one is an actual committed decision.
| Dimension | ScorePropensity score | RouterRouting agent | Static logicRules engine | Reasoning layerAI Decision Engine |
|---|---|---|---|---|
| What it outputs | A probability between 0 and 1 | A handoff between systems or agents | A triggered action from if-then logic | The next best commercial move, per customer |
| Commits a decision? | No: it ranks likelihood, a human still decides | No: it moves messages, not decisions | Only what a human pre-wrote | Yes: it reasons and commits the decision |
| Rationale attached | No: just the number | No | No: the rule is the only explanation | Yes: the rationale travels with the decision |
| Granularity | Per model, not per moment | Per message | Per segment or trigger | Per individual customer, continuously |
| Adapts over time | Retrained in batches | Static routing logic | Static until a human edits it | Learns from every outcome |
A score ranks, a router forwards, a rule fires. An AI Decision Engine reasons and commits, with the rationale attached.
FAQ
A propensity score is a probability that ranks how likely something is; a human still has to decide what to do about it. An AI Decision Engine reasons over the customer's context and commits to the next best action itself, with the rationale attached, so the decision is ready to execute.