AI-Powered Dynamic Pricing Case Study

The challenge

A major retail chain needed to modernise its pricing operations to stay competitive in a fast-moving market. Prices were still being adjusted manually across every location - a process that couldn't keep pace with real-world demand.

⚠️ Manual price adjustments caused delays and errors. Every price change had to be manually reviewed and applied across locations, introducing lag between a pricing decision and it actually reaching the shelf or checkout - and leaving room for inconsistency between stores.

⚠️ No AI-driven insight into market responsiveness. Without any intelligence layer over sales and demand data, the business had no way to react to real-time market signals - pricing was reactive at best, and often simply out of date.

⚠️ Pricing wasn't optimised against demand patterns. There was no systematic way to align pricing with observed demand trends, so the retailer was leaving margin and conversion opportunities on the table during both high- and low-demand periods.

⚠️ No scalable way to manage pricing across locations. Thousands of SKUs across multiple locations meant that any manual process would only get more unmanageable as the retailer grew - a fundamentally different architecture was needed, not just a faster spreadsheet.

Pricing architecture

Automated Pricing Orchestration Engine

Manual pricing steps remaining

1000s

SKUs priced automatically

Real-time

Demand-responsive adjustments

Sales & Demand Data → Kafka Event Stream → AI Pricing Model → Rules Engine → Redis Cache → Price Published

Our solution

OKRUTI built an automated pricing orchestration engine that puts AI-driven analysis at the centre of every pricing decision, wrapped in a microservices architecture built to scale across every store and SKU.

→ AI-Powered Demand Analytics: AI models continuously analyse sales history and demand patterns across every location, recommending optimal pricing in real time - replacing manual judgement calls with data-driven recommendations that update as conditions change.

→ Microservices on Java & Spring Boot: The pricing platform was built as a set of independently scalable services, so pricing logic, demand analysis, and publication can each scale according to their own load - not the system as a whole.

→ Event-Driven Processing with Kafka: Sales and demand signals flow through Kafka topics, enabling real-time price updates across all outlets the moment new data arrives, instead of on a batch schedule.

→ Logic-Driven Rules Engine: AI recommendations are validated against a configurable rules engine that enforces business policy - margin floors, promotional boundaries, and brand-specific constraints - before any price goes live.

→ Redis Caching Layer: Active pricing data is cached in Redis for high-speed retrieval, so price lookups at checkout and on digital shelves stay fast even as the catalogue and location count grow.

Results achieved

100%

Manual pricing processes eliminated - pricing is now fully automated end to end

1000s

SKUs priced consistently across multiple locations without manual intervention

Real-time

Dynamic pricing adjustments that respond to market conditions as they change

AI-powered recommendations free operational teams to focus on strategy, not manual updates

Need AI-driven pricing or demand intelligence?

Whether you're replacing a manual pricing process or adding AI-driven intelligence to an existing engine, our team can design the right architecture for your catalogue and scale.

Book Free AI Consultation → All AI solutions

Automated Pricing Orchestration
for a Leading Retail Chain

Need AI-driven pricing or demand intelligence?

Let's talk about your platform.

Automated Pricing Orchestrationfor a Leading Retail Chain

Need AI-driven pricing or demand intelligence?

Related case studies

Let's talk about your platform.

Automated Pricing Orchestration
for a Leading Retail Chain