← Selected Work
Global Games PublisherEntertainment · GamingBuild · Production2025

Pricing intelligence for games & digital assets.

Traditional AI optimises pricing across games and digital assets; a multi-agent GenAI layer turns real-time data into financial, risk, strategy and competitor insight.

Pricing copilot · multi-agent
Client
Subsidiary · $31.7B rev · 12.7k staff
Duration
6 months
Scope
Production
Stack
OpenAI · CrewAI
Architecture
ML + multi-agent
Year
2025
The challenge

A global games publisher needed to optimise pricing across its catalogue of video games and digital assets — and to make sense of pricing scenarios against fast-moving, real-time market data.

Our approach

We paired traditional ML for price optimisation with a GenAI multi-agent layer that reasons over scenarios and live data — generating insight a pricing team can act on, not just a number.

What we built

A pricing optimisation engine (traditional AI) plus four GenAI agents that produce insight and reports on pricing scenarios and real-time data: a Financial Analysis agent, a Risk Analysis agent, a Strategy agent, and a Trend & Competitor Analysis agent.

Tech stack
Models & agents
OpenAICrewAIMulti-agent
ML
Price-optimisation modelsReal-time data pipeline
Insight agents
Financial analysisRisk analysisStrategyTrend & competitor
What we delivered
  • A price-optimisation engine for games & digital assets
  • Four GenAI insight agents (financial · risk · strategy · trend)
  • Real-time pricing-scenario reporting
  • Delivered to production in 6 months (2025)
Timeline
Phase 1
Pricing-model design & data
Build
Optimisation engine + agent layer
6 months
To production
At a glance
4
GenAI insight agents
Real-time
Market-data scenarios
Prod
Live in production
Outcomes
4
Insight agents in production
Real-time
Scenario & competitor insight
6mo
To production
Next case →Fan-engagement intelligence for a global music label.

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