Independent analytics & AI consultant
I design and deploy production-grade decision systems for pricing, marketing measurement, forecasting, and commercial intelligence.
Decision systems are the Artometrix frame: models embedded with constraints, feedback loops, accountability, and operating cadence.
Worked with
countries
MMM in production
locations
forecasting pipeline live
sectors
luxury / pharma / fintech
tools
interactive, open, in-browser
What I help with
Demand forecasting, scenario planning, inventory strategy, and operational decisions under uncertainty.
Typical deliverable
Forecasting system in production, 30-90 day cadence.
Pricing optimization, elasticity modeling, portfolio trade-offs, and decision support for teams.
Typical deliverable
Pricing decision system with elasticity, constraints, and portfolio trade-offs.
MMM, response curves, budget allocation, and shared finance-marketing decision systems.
Typical deliverable
MMM and budget allocation system on a monthly review cadence.
AI workflows, recommendation systems, and auditable business-rule-driven decision logic.
Typical deliverable
Auditable decision workflow with business-rule logic and a clear handover.
How I work
01 · FRAME
Define the decision, the constraints, and the operating cadence. Most projects fail here before any modeling starts.
02 · MODEL
Build the system embedded with feedback loops and accountability. Not a notebook, not a prototype.
03 · DEPLOY
Move from a working model to a production decision system the team can actually run.
04 · OPERATE
Hand over with monitoring, drift controls, and the documentation to keep the system honest.
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.
Enterprise AI systems often work at the first level of granularity, then become fragile when the business asks for more precision. A field lesson from pharmaceutical supply optimization on why incremental architectures matter.
Raw latency and perceived latency are different engineering problems. Production GenAI systems feel fast when they expose progress early, overlap backend work, and avoid silent waiting.
What it actually takes to build a production forecasting system for luxury demand at SKU level: decomposing products into shared attributes, designing hierarchical aggregate features without leakage, handling intermittency with Tweedie and hurdle objectives, and forecasting in a way finance can use.
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.
A field perspective on hierarchical forecasting in luxury retail — where ultra-premium SKUs sell fewer than 100 units a year and finance cannot ignore them.
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.
Tools
Interactive products for marketers, analysts, and operators: response curves, adstock, CLV, and budget optimization.
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.