Gift card balances, store credit, and promo codes are where ecommerce AI builds or breaks trust. This post covers what AI needs to get right with payment handling, and the intentional boundary where it should stop.
Most AI support platforms talk about what their bot can do. Alhena is engineered so that seven specific ticket categories never auto-resolve end-to-end, protected by a 10-layer gating architecture.
Yuma AI excels at support automation with 89% ticket deflection rates. Alhena AI focuses on revenue attribution, driving 3x conversions and 38% AOV uplift. Compare both platforms across features, pricing, integrations, and real-world case studies.
Traditional APM wasn't built for non-deterministic AI pipelines. Alhena runs dual tracing (Opik for AI, OpenTelemetry for infrastructure), captures 13 timing measurements per message, and uses a ConversationDebuggerAgent to explain why the bot said what it said.
When customers ask whether they can combine a promo code with an active sale, the answer depends on discount types, platform rules, and cart contents. AI built for ecommerce resolves these questions in real time by connecting to live catalog data and enforcing configurable stacking rules.
Most e-commerce AI is a black box. Alhena's Conversation Debugger changes that by tracing every AI response back to the exact product data, knowledge base article, or policy document that grounded it. Here's how the three-layer system works.