Independent consultant
I help retail, healthcare, product, and marketing teams frame, build, and deploy analytical systems.
Advisory, system build, or embedded support. From problem framing through production deployment.
across data and decision systems
plus FMCG, Media and FinTech experience
What I help with
Demand forecasting, scenario planning, inventory strategy, and operational decisions under uncertainty.
Pricing optimization, elasticity modeling, portfolio trade-offs, and decision support for teams.
MMM, response curves, budget allocation, and shared finance-marketing decision systems.
AI workflows, recommendation systems, and auditable business-rule-driven decision logic.
Decisions are the product. Software is the tool.
Forecasting, pricing, MMM, and AI decision workflows designed for operations.
Selected work
A few examples across forecasting, MMM, and operational risk.
A production-oriented recommendation system that guides customers through emotionally loaded floral purchases — using a deterministic state machine with LLM components constrained to intent parsing and rationale generation only.
Key resultFull guided purchase flow: delivery validation, persona selection, budget extraction, curated recommendations with auditable scoring
Multi-SKU demand forecasting pipeline for 30+ products across 100+ duty-free locations with automated monthly updates.
Key resultForecast error: 38% → 24%
Proprietary Marketing Mix Model with budget optimization replacing intuitive allocation with data-driven decision making across multiple countries and touchpoints.
Key resultStrategic budget reallocation based on incremental response curves
Writing
The writing is there as proof of depth, not as a substitute for the offer.
Intelligence-native systems need agent access to decision artefacts and feedback loops. Why context, not models, is the differentiator — and how MCP, traditional ML, and versioned artefacts fit together.
Markdown planning explodes when weeks, events, discount levels, and phases are modeled as separate dimensions. Collapsing each week into a single state turns it into a clean optimization problem.
Seasons, campaigns, and weekly events — retail runs on overlapping cycles, and the AI recommender has to keep up with all of them. Notes on the business-rules control surface that lets merchandising teams steer a conversational recommender without editing prompts, filing tickets, or waiting for a deploy.
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 ?'
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.
Tools
Interactive tools and calculators for practitioners — built to explore ideas, not to sell software.
Marketing saturation curves
Marketing carryover effects
Budget optimization
Customer lifetime value
Marketing efficiency zones
Market share & loyalty
The most useful early step is usually clarifying the decision, the constraints, and what actually needs to be productionized.