Play 87

Dynamic Pricing Engine

High✓ Designed

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.

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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

Machine evidence

FrootAI evidence lifecycle

This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.

Designed

designed

build verified

evaluation verified

This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.

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Repo Intelligence

A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.

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