Berlin AI Engineering Group by m|rig

Companies across retail, logistics, e-commerce, and financial services are increasingly turning to AI-driven dynamic pricing to react to demand, optimize margins, and compete in fast-moving markets.
But how do you actually design, build, and deploy a dynamic pricer that is robust, data-driven, and aligned with business goals?
In this 60-minute interactive webinar, we’ll walk through the end-to-end design of an AI-based pricing engine—from data architecture to the ML algorithms that power real-time price recommendations.
You will also see a live demonstration of a fully functional dynamic pricer based on a real-world use case.

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You’ll learn

🧩 Core Concepts of Dynamic Pricing with AI
How modern AI pricing systems differ from rule-based models, and which business contexts benefit most from dynamic pricing.

🏗️ Data Architecture for Pricing Engines
How to design a scalable data pipeline for pricing:

• internal & external data sources
• feature engineering for pricing
• model training and inference

🤖 Machine Learning Models for Pricing
A clear overview of the ML techniques commonly used in pricing, including:

• demand forecasting models
• price elasticity estimation
• how to measure uplift and avoid bias

💡 Live Demo: Real-World Dynamic Pricer
Watch a full pricing engine in action:

• data flows & features
• price output
• impact on revenue and margins