Security, availability, and confidentiality - independently audited to meet enterprise data protection standards.
ReactorCX was built for complexity. That turned out to be exactly what AI needs to work at depth.
ReactorCX was built API-first, fully programmable, and open at every layer. Every rule, earn rate, tier threshold, and reward definition is structured and directly accessible through the same infrastructure your systems already use.
That architectural discipline — built for enterprise reliability and scale — is exactly what AI needs to reason about loyalty programs with depth and precision. The platform speaks the language AI speaks natively: structured data, machine-readable rules, and well-defined API contracts.
When AI connects to ReactorCX through the Model Context Protocol, it reads your program the way an engineer would: rule conditions, tier qualification logic, version history, earn rate structures.
The platform was already built to be read that way. ReactorCX's structured, machine-readable configuration gives AI complete program context, turning architectural discipline into operational intelligence.
Every loyalty program runs on trust: with members, with finance, with compliance. ReactorCX keeps a human in the loop at every decision point. AI brings the analysis. Your team makes the call. The engine executes with precision.
AI reads your live program configuration — earn rules, tier thresholds, and campaign logic — and surfaces insights your team would otherwise spend hours finding. It flags configuration gaps, rule conflicts, and what to address next.
Your marketer reviews the insight in plain language, asking follow-up questions, stress-testing assumptions, and refining the approach. AI responds with reasoning grounded in your actual data.
Once direction is set, AI drafts the full structured change — a promotion update or rule modification — complete with projected cost impact, affected members, and a summary ready for sign-off.
Your team gives the final sign-off. Only then does the platform engine execute — with full precision, full audit trail, and zero ambiguity. Every point awarded, every rule change is logged and traceable.
Two distinct capabilities. One integrated platform.
Neither replaces the other.
Think of it as assisted driving, not self-driving, for loyalty. When a misconfigured rule can burn through millions in points before anyone catches it, you want human hands on the wheel.
AI capability embedded at every stage of the loyalty lifecycle,
from initial program design through continuous optimization.
Your team describes what they want in plain language. The AI reads your live program configuration, maps intent to the correct rules and promotion mechanics, and returns a fully structured proposal for review.
Before a promotion goes live, the AI models member participation, projects cost, identifies rule conflicts, and flags anything likely to behave unexpectedly at scale. Analysis that used to require an analyst and a spreadsheet runs in a conversation.
Ask your program anything in plain language. Earn rates across properties, tier qualification thresholds, what a specific member earned last month and why. The AI reads the same configuration data your engineers do, returned in plain language.
The AI monitors program health, correlates transaction patterns against your configuration, and surfaces anomalies. Underperforming segments and misaligned earn rates get pushed to your dashboard before they become problems.
A member didn't receive points. A promotion didn't fire. The AI traces the full transaction path, identifies exactly where something failed, and tells you what to fix. Issues that took hours resolve in a conversation.
Call center teams get answers grounded in the member's actual program record, without escalating, querying a database, or waiting. Your team asks in plain language. The member gets an answer on the call.
Connect to your organization's preferred large language model. Your proprietary program data flows through your own approved infrastructure, governed by your own data handling policies.
AI reads the program the way it was always meant to be read: through explicit, machine-readable rules and models that document themselves.
