Writing on how decisions get modeled, built, and deployed.
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.
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.
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.
LangChain, LangGraph, Claude ADK, Google ADK, PydanticAI — different names, different abstractions, same promise. This article cuts through the framework noise and identifies the five architectural properties that actually determine whether an LLM system survives production.
LLMs can extract structured data from anything — until they cannot. This article documents the failure modes of LLM-only parsing in production pipelines, and presents a layered architecture where determinism comes first and the LLM is used only where it is structurally irreplaceable.
La plupart des échecs GenAI en entreprise ne viennent pas d'un mauvais modèle. Ils viennent d'un mauvais design de workflow, d'une absence de mandat et d'une architecture incapable de faire agir un système.
Most companies are not ready for autonomous commerce agents. The practical starting point is Agentic RAG: systems that retrieve business context, reason over it, and produce decision-ready outputs.
Two initiatives can show the same ROI and still deserve very different decisions. The missing variable is time-to-ROI: how fast the initial investment is recovered.
Sales volume mixes demand and inventory effects. Sell-through rate captures what actually matters: how fast inventory clears under pricing and context constraints.
Interactive tools and calculators for practitioners — built to explore ideas, not to sell software.
Available for consulting engagements in forecasting, pricing, and decision systems. More about me