Play 97
AI Data Marketplace
High✅ Ready
Platform for publishing, discovering, and monetizing synthetic and anonymized datasets.
Platform for publishing, discovering, and monetizing synthetic and anonymized datasets. Differential privacy validation, statistical fidelity scoring, usage-based billing, and API-first data access for AI training and testing workflows.
Architecture Pattern
Data marketplace flow: dataset publishing - privacy validation - fidelity scoring - catalog indexing - usage billing - API access
Azure Services
Azure Machine LearningAzure Blob StorageAzure API ManagementAzure Cosmos DBAzure Functions
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Marketplace Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (229 lines), evaluate (104 lines), tune (230 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + AI Search inputs + envFile
TuneKit (AI Config)
- config/openai.json - dataset description and quality analysis prompts
- config/marketplace.json - pricing models, privacy budgets, access tiers
- config/guardrails.json - privacy epsilon limits, fidelity minimums
- evaluation/eval.py - Privacy compliance 100%, Fidelity score >0.90
Tuning Parameters
Privacy epsilon budgetPricing model configData quality thresholdsAccess tier definitionsUsage metering interval
Estimated Cost
Dev/Test
$100-250/mo
Production
$3K-10K/mo