Play 97
AI Data Marketplace
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
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
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.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A 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.