Play Browser · 101 plays
Solution plays
Production-grade FAI Protocol plays. Each ships with a manifest, infrastructure code, primitives, and an evaluation suite. All MIT.
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- 49-creative-ai-studiolargeOptimize creative temperature, variation diversity, brand voice fidelity, A/B test performance, cross-platform adaptation quality
Optimize creative temperature, variation diversity, brand voice fidelity, A/B test performance, cross-platform adaptation quality
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 50-financial-risk-intelligencelargeOptimize fraud decision thresholds, risk score accuracy, LLM usage ratio for edge cases, latency for real-time scoring, fairness constraints
Optimize fraud decision thresholds, risk score accuracy, LLM usage ratio for edge cases, latency for real-time scoring, fairness constraints
- primitives
- 10
- waf
- 6
- industry
- general
- azure
- 53-legal-document-aimediumOptimize clause detection accuracy, risk score calibration, redline quality, processing throughput for large contracts, clause library coverage
Optimize clause detection accuracy, risk score calibration, redline quality, processing throughput for large contracts, clause library coverage
- primitives
- 10
- waf
- 4
- industry
- general
- azure
- 57-ai-translation-enginemediumOptimize LLM refinement ratio, quality score thresholds, glossary coverage, batch throughput, cost per 1K words
Optimize LLM refinement ratio, quality score thresholds, glossary coverage, batch throughput, cost per 1K words
- primitives
- 10
- waf
- 4
- industry
- general
- azure
- 58-digital-twin-agentmediumOptimize NL→DTDL query accuracy, predictive maintenance confidence, sensor refresh rate, telemetry archival cost, twin update throughput
Optimize NL→DTDL query accuracy, predictive maintenance confidence, sensor refresh rate, telemetry archival cost, twin update throughput
- primitives
- 10
- waf
- 4
- industry
- general
- azure
- 59-ai-recruiter-agentmediumOptimize matching accuracy, reduce disparate impact, calibrate scoring weights, tune PII detection recall, minimize cost per screening
Optimize matching accuracy, reduce disparate impact, calibrate scoring weights, tune PII detection recall, minimize cost per screening
- primitives
- 10
- waf
- 4
- industry
- general
- azure
- 60-responsible-ai-dashboardmediumOptimize monitoring cadence, alert thresholds, executive summary quality, report scheduling, intersectional group coverage
Optimize monitoring cadence, alert thresholds, executive summary quality, report scheduling, intersectional group coverage
- primitives
- 10
- waf
- 4
- industry
- general
- azure
- 63-fraud-detection-agentlargeOptimize detection thresholds per transaction type, reduce false positives, tune velocity windows, calibrate ML model, improve graph analysis depth
Optimize detection thresholds per transaction type, reduce false positives, tune velocity windows, calibrate ML model, improve graph analysis depth
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 64-ai-sales-assistantmediumOptimize scoring weights for conversion correlation, email personalization depth, talk track relevance, CRM sync frequency, cost per lead
Optimize scoring weights for conversion correlation, email personalization depth, talk track relevance, CRM sync frequency, cost per lead
- primitives
- 10
- waf
- 4
- industry
- general
- azure
- 66-ai-infrastructure-optimizermediumOptimize utilization thresholds, cost anomaly sensitivity, analysis frequency, auto-scale triggers, GPU-to-CPU migration criteria
Optimize utilization thresholds, cost anomaly sensitivity, analysis frequency, auto-scale triggers, GPU-to-CPU migration criteria
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 67-ai-knowledge-managementlargeOptimize capture rate from sources, dedup similarity threshold, taxonomy depth, freshness TTL, expertise scoring weights, retrieval relevance
Optimize capture rate from sources, dedup similarity threshold, taxonomy depth, freshness TTL, expertise scoring weights, retrieval relevance
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 68-predictive-maintenance-ailargeOptimize RUL thresholds (urgent/planned/monitor), feature selection, model retrain frequency, alert aggregation, cost of downtime vs maintenance
Optimize RUL thresholds (urgent/planned/monitor), feature selection, model retrain frequency, alert aggregation, cost of downtime vs maintenance
- primitives
- 10
- waf
- 5
- industry
- general
- azure