IA Générative11 min de lecture

Ce que le commerce agentique exige vraiment en production

Macy's annonce que les clients utilisant son assistant IA dépensent 4,75x plus. Sephora vient de lancer une app dans ChatGPT. Zalando déploie son assistant dans 25 marchés. La question pour tous les autres retailers n'est plus 'faut-il le faire ?' mais 'comment l'architecturer pour que ça tienne en production ?'

Cyril Noirot

Lead Data Scientist

13 avril 2026

À propos de l'auteur

Cyril Noirot

Lead Data Scientist

Data scientist freelance. Je conçois et déploie des systèmes de décision — prévision, pricing, marketing measurement, optimisation — pour des équipes qui prennent des décisions à fort enjeu.

Plus sur Cyril Réalisations LinkedIn

Who controls your AI recommender in production?

Most AI commerce demos look great until you ask: who controls the ranking next Monday? This article walks through the operator-control architecture I built for a fashion recommendation system — hot-reloadable business rules, multi-agent persona routing, and a catalog analytics layer that lets merchandisers query their own data in plain French.

IA Générative11 min

Building a recommendation engine that doesn't trust the LLM

This is the engineering companion to the production architecture piece. Instead of re-arguing why open-ended agents are risky in commerce, it walks through the actual implementation choices in `ai-florist`: FastAPI boundaries, LangGraph orchestration, pgvector retrieval, learned scoring weights, deterministic fallbacks, and runtime observability.

IA Générative10 min

What agentic commerce actually requires in production

Macy's reports customers spending 4.75x more with their AI assistant. Sephora just launched inside ChatGPT. Zalando rolled out to 25 markets. The question for every other retailer isn't whether to build this — it's how to architect it so it actually works. Here's what I learned building one for premium floral e-commerce.

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