Play 87
Dynamic Pricing Engine
High✅ Ready
Real-time price optimization with demand signals and fairness guardrails.
Real-time price optimization engine analyzing demand signals, competitor pricing, inventory levels, seasonality, and customer segments to maximize revenue while maintaining price fairness and brand perception. OpenAI powers pricing intelligence and competitor analysis, Event Hubs ingests real-time demand signals, Cosmos DB stores pricing rules and history, Redis Cache provides sub-millisecond price lookups, and Machine Learning builds elasticity models.
Architecture Pattern
Dynamic pricing pipeline: demand signals - competitor monitoring - elasticity modeling - fairness checks - price optimization - real-time updates
Azure Services
Azure OpenAIAzure Event HubsAzure Cosmos DBAzure Redis CacheAzure Machine Learning
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Pricing Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (231 lines), evaluate (122 lines), tune (239 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json - pricing intelligence and competitor analysis prompts
- config/pricing.json - elasticity models, inventory weights, seasonal rules
- config/guardrails.json - fairness thresholds, price ceiling/floor rules
- evaluation/eval.py - Revenue lift >5%, Fairness score >90%
Tuning Parameters
Price elasticity modelCompetitor monitoring frequencyInventory weight factorFairness guardrailsSeasonal adjustment rules
Estimated Cost
Dev/Test
$100-250/mo
Production
$3K-10K/mo