Growth Strategist, Performance Marketer, Developer, Data Scientist and Unicorn.
Hi, I'm Georg, a London based Growth Hacker, Data Scientist, Developer, and most recently AI Engineer with 14 years of experience and a hands on approach to turning ideas into thriving businesses. I've helped startups, ecommerce stores, and SMEs achieve fast yet sustainable growth through data driven strategy and practical execution. What sets me apart is simple: I don't just talk the talk, I walk the walk. I execute directly, cutting out inefficiencies and delivering measurable results. I primarily offer my services on a commission basis, effectively covering my own cost, because that's how confident I am in what I do.
The dynamic pricing software market is projected to grow from $6.16 billion in 2025 to $41.43 billion by 2033, and 55% of retailers plan to implement AI pricing in 2026. Amazon changes prices 2.5 million times a day. You don't need to be Amazon. This post breaks down how ML-driven dynamic pricing actually works - price elasticity estimation, demand signals, competitor monitoring, and margin guardrails - with practical Solidus/Rails code and Python scripts you can run today.
Traffic from AI sources like ChatGPT, Perplexity, and Gemini to ecommerce sites went up 3,300% year-over-year on Prime Day 2025. During the holiday season, AI referrals to retail sites jumped 693%. And here's the kicker - those AI-referred shoppers converted 31% more than visitors from other sources, spent 45% more time on site, and viewed 13% more pages. This isn't a novelty. It's a new discovery channel. This post covers what it means for SEO, product feeds, and how retailers like New Look and Selfridges need to adapt their product data strategy.
IKEA's Demand Sensing tool halved their forecast error rate from 8% to 2% by using up to 200 data sources per product. That's what AI-powered demand forecasting looks like at scale. But you don't need IKEA's budget to get meaningful results. This post compares three forecasting approaches - Prophet, SARIMA, and XGBoost - with practical examples from Solidus ecommerce and custom SaaS, showing which model works best for which scenarios and how to implement them in a Rails-based product stack.
Third-party cookies are dead, browser tracking is gutted, and regulators are fining companies hundreds of millions for getting consent wrong. But personalisation still works - it just needs a different foundation. This post covers consent-based personalisation, server-side tracking architecture, first-party and zero-party data strategies that actually perform, and the practical Rails code to make it all work across the DACH market. Real examples from four products serving Austrian, German, and Swiss users, with jurisdiction-aware consent handling built in.
If you're building AI-powered SaaS for the DACH market, you're not dealing with one regulatory framework. You're dealing with at least five: the EU AI Act (directly applicable in Austria and Germany), GDPR (enforced differently by each country's DPA), Austria's Digital Austria Act 2.0 and KI-Servicestelle, Germany's KI-MIG implementation law and Bundesnetzagentur oversight, and Switzerland's entirely separate FADP with its own rules on AI, profiling, and personal liability. This post maps the specific nuances for Austrian, German, and Swiss businesses, shows where the regulations overlap and where they diverge, and provides the practical architecture decisions that let you ship AI features across all three markets from a single Rails codebase.
A developer's practical walkthrough of Data Processing Agreements, Privacy by Design, and Data Protection Impact Assessments for AI features. Not the legal theory, the actual Rails code and architecture decisions you need to make before shipping AI features to production. With real examples from four production Ruby on Rails applications: GrowCentric.ai (marketing optimisation), Stint.co (marketing dashboard), Regios.at (regional platform), and Auto-Prammer.at (automotive marketplace on Solidus).
