Generative AI4 min read

Why Smarter AI Does Not Automatically Mean Better Architecture

Modern LLMs are objectively better at nuanced language tasks than legacy systems. But when you zoom out from a single prompt in a playground to a production environment processing millions of requests, the paradigm shifts entirely. Here is why using an LLM as a universal parser and router is often the wrong architectural choice.

Cyril Noirot

Lead Data Scientist

February 25, 2026

About the author

Cyril Noirot

Lead Data Scientist

Freelance data scientist. I design and ship decision systems — forecasting, pricing, marketing measurement, optimization — for teams making high-stakes calls.

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.

Generative AI11 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.

Generative AI10 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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