1282 lines
47 KiB
JSON
1282 lines
47 KiB
JSON
{
|
||
"id": "Enrich360",
|
||
"name": "Enrich360",
|
||
"description": "Optimize qualification effectiveness for tiered customer programs using AI-driven insights to boost engagement and profitability.",
|
||
"status": "87% Complete",
|
||
"statusType": "progress",
|
||
"tabs": [
|
||
{
|
||
"name": "Overview",
|
||
"description": "Optimize qualification effectiveness for tiered customer programs using AI-driven insights to boost engagement and profitability.",
|
||
"goals": [
|
||
"Increase overall qualification effectiveness by 10%",
|
||
"Enhance customer engagement metrics by optimizing tier criteria"
|
||
],
|
||
"objective": "Leverage a self-directed Multi-Agent System to achieve improvements in qualification rates and customer activity.",
|
||
"roles": [
|
||
{
|
||
"name": "Product Manager",
|
||
"description": ""
|
||
}
|
||
],
|
||
"benefits": [
|
||
"Provide proactive recommendations on tier adjustments to anticipate customer behaviors",
|
||
"Support data-driven decisions for better resource allocation and feature bundling",
|
||
"Identify opportunities to reduce passive qualifications and increase active engagements",
|
||
"Improve customer retention by aligning tiers with usage patterns and feedback"
|
||
]
|
||
},
|
||
{
|
||
"name": "Bill of Materials",
|
||
"description": "Bill of Materials - Visual representation of Multi-Agent System architecture and component relationships.",
|
||
"data": {
|
||
"orchestrator": [
|
||
{
|
||
"id": "orchestrator-1",
|
||
"name": "Enrich 360 Lyza Agent",
|
||
"type": "ORCHESTRATOR_AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {},
|
||
"upstreamConnections": [],
|
||
"downstreamConnections": [
|
||
"agent-offers-and-rewards",
|
||
"agent-balance-prediction",
|
||
"agent-product-optimization",
|
||
"agent-competitor-insights",
|
||
"agent-tier-movement",
|
||
"agent-qualification-effectiveness",
|
||
"agent-customer-segmentation",
|
||
"agent-churn-management"
|
||
]
|
||
}
|
||
],
|
||
"agents": [
|
||
{
|
||
"id": "agent-offers-and-rewards",
|
||
"name": "Offers and Rewards Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Designs personalized incentives to boost engagement and retention"
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"2773",
|
||
"model-churn-mitigation"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-churn-management",
|
||
"name": "Churn Management Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Predicts and mitigates customer attrition using behavioral patterns"
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"2773",
|
||
"model-churn-mitigation"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-balance-prediction",
|
||
"name": "Balance Forecasting Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Forecasts account balances using historical and transactional data trends."
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"model-balance-prediction"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-product-optimization",
|
||
"name": "Product Optimization Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Identifies ideal pricing strategies for products to maximize revenue and satisfaction"
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"model-product-optimization"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-competitor-insights",
|
||
"name": "Competitor Insights Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Analyzes competitor trends to uncover market opportunities and threats."
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"tool-kb",
|
||
"tool-epm-mcp"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-tier-movement",
|
||
"name": "Tier Movement Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Tracks and predicts customer movement across loyalty or account tiers."
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"model-tier-movement"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-qualification-effectiveness",
|
||
"name": "Qualification Effectiveness Agent",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Evaluates effectiveness of customer qualification criteria."
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"model-balance-prediction",
|
||
"model-product-optimization",
|
||
"model-usage-classification",
|
||
"model-tier-movement",
|
||
"model-usage-forecasting",
|
||
"model-customer-segmentation",
|
||
"model-refund-forecasting",
|
||
"tool-lakehouse",
|
||
"tool-kb",
|
||
"tool-chart-generation",
|
||
"tool-web-search",
|
||
"tool-epm-mcp"
|
||
]
|
||
},
|
||
{
|
||
"id": "agent-customer-segmentation",
|
||
"name": "Customer Segmentation",
|
||
"type": "AGENT",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"description": "Clusters customers by behavior, value, and engagement for targeting."
|
||
},
|
||
"upstreamConnections": [
|
||
"orchestrator-1"
|
||
],
|
||
"downstreamConnections": [
|
||
"model-similar-accounts",
|
||
"model-usage-classification"
|
||
]
|
||
}
|
||
],
|
||
"models": [
|
||
{
|
||
"id": "2773",
|
||
"name": "Likelihood of Churn Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 8,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-offers-and-rewards"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-churn-mitigation",
|
||
"name": "Churn Mitigation Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 10,
|
||
"size": "1.5 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-offers-and-rewards"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-balance-prediction",
|
||
"name": "Balance Prediction Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 12,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-balance-prediction",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-balance-history"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-refund-forecasting",
|
||
"name": "Refund Forecasting Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 12,
|
||
"size": "2 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-product-optimization"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-product",
|
||
"fs-balance-history",
|
||
"fs-event-history",
|
||
"fs-monthly-deposit"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-product-optimization",
|
||
"name": "Product Optimization Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 8,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-product-optimization",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-product",
|
||
"fs-account",
|
||
"fs-customer-segmentation",
|
||
"fs-event-history",
|
||
"fs-eligible-offers"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-tier-movement",
|
||
"name": "Tier Movement Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 10,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-tier-movement",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-product",
|
||
"fs-balance-history",
|
||
"fs-customer-segmentation",
|
||
"fs-account",
|
||
"fs-event-history",
|
||
"is-tier"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-customer-segmentation",
|
||
"name": "Customer Segmentation Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 10,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-customer-segmentation",
|
||
"fs-account",
|
||
"fs-product",
|
||
"fs-balance-history",
|
||
"is-engagement"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-usage-classification",
|
||
"name": "Usage Classification Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 8,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-customer-segmentation",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-monthly-deposit"
|
||
]
|
||
},
|
||
{
|
||
"id": "model-usage-forecasting",
|
||
"name": "Usage Forecasting Model",
|
||
"type": "MODEL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"parameters": 8,
|
||
"size": "1 GB"
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-monthly-deposit",
|
||
"fs-event-history"
|
||
]
|
||
}
|
||
],
|
||
"tools": [
|
||
{
|
||
"id": "tool-batch",
|
||
"name": "Batch job",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 4
|
||
},
|
||
"upstreamConnections": [
|
||
"2773"
|
||
],
|
||
"downstreamConnections": [
|
||
"fs-churn"
|
||
]
|
||
},
|
||
{
|
||
"id": "tool-chart-generation",
|
||
"name": "Chart Generation Tool",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 4
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-tier-movement",
|
||
"model-qualification-effectiveness",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": []
|
||
},
|
||
{
|
||
"id": "tool-epm-mcp",
|
||
"name": "EPM MCP tool",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 2
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-qualification-effectiveness",
|
||
"agent-competitor-insights"
|
||
],
|
||
"downstreamConnections": []
|
||
},
|
||
{
|
||
"id": "tool-model-inference",
|
||
"name": "Model inference tool",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 3
|
||
},
|
||
"upstreamConnections": [
|
||
"model-qualification-effectiveness",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": []
|
||
},
|
||
{
|
||
"id": "tool-web-search",
|
||
"name": "Web Search Tool",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 2
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-competitor-insights",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": []
|
||
},
|
||
{
|
||
"id": "tool-kb",
|
||
"name": "Knowledge Base Tool",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 2
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-competitor-insights",
|
||
"agent-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": [
|
||
"kb-first-citizen-web-data",
|
||
"kb-capitalone-web-data",
|
||
"kb-chase-web-data",
|
||
"kb-td-web-data"
|
||
]
|
||
},
|
||
{
|
||
"id": "tool-lakehouse",
|
||
"name": "Lakehouse Connector Tool",
|
||
"type": "TOOL",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"interfaces": 2
|
||
},
|
||
"upstreamConnections": [
|
||
"agent-qualification-effectiveness",
|
||
"model-qualification-effectiveness"
|
||
],
|
||
"downstreamConnections": []
|
||
}
|
||
],
|
||
"dataEntities": [
|
||
{
|
||
"id": "entity-customer",
|
||
"name": "Customer",
|
||
"type": "DATA_ENTITY",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"fields": 15
|
||
},
|
||
"upstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn",
|
||
"fs-monthly-deposit",
|
||
"is-qualification",
|
||
"fs-tier-history",
|
||
"fs-eligible-offers",
|
||
"fs-account",
|
||
"fs-customer-segmentation"
|
||
],
|
||
"downstreamConnections": [
|
||
"POSTGRES/EPM_CA"
|
||
]
|
||
},
|
||
{
|
||
"id": "entity-account",
|
||
"name": "Account",
|
||
"type": "DATA_ENTITY",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"fields": 12
|
||
},
|
||
"upstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn",
|
||
"fs-tier-history",
|
||
"fs-balance-history",
|
||
"fs-account",
|
||
"fs-customer-segmentation"
|
||
],
|
||
"downstreamConnections": [
|
||
"POSTGRES/EPM_CA"
|
||
]
|
||
},
|
||
{
|
||
"id": "entity-transactions",
|
||
"name": "Transactions",
|
||
"type": "DATA_ENTITY",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"fields": 18
|
||
},
|
||
"upstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn",
|
||
"fs-monthly-deposit",
|
||
"fs-customer-segmentation",
|
||
"fs-event-history"
|
||
],
|
||
"downstreamConnections": [
|
||
"POSTGRES/EPM_CA"
|
||
]
|
||
},
|
||
{
|
||
"id": "entity-balances",
|
||
"name": "Balances",
|
||
"type": "DATA_ENTITY",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"fields": 21
|
||
},
|
||
"upstreamConnections": [
|
||
"fs-tier-history",
|
||
"fs-event-history",
|
||
"fs-balance-history",
|
||
"fs-monthly-deposit"
|
||
],
|
||
"downstreamConnections": [
|
||
"POSTGRES/EPM_CA"
|
||
]
|
||
},
|
||
{
|
||
"id": "entity-products",
|
||
"name": "Products",
|
||
"type": "DATA_ENTITY",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"fields": 8
|
||
},
|
||
"upstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn",
|
||
"fs-eligible-offers",
|
||
"fs-tier-history",
|
||
"fs-product",
|
||
"fs-customer-segmentation",
|
||
"fs-event-history"
|
||
],
|
||
"downstreamConnections": [
|
||
"POSTGRES/EPM_CA"
|
||
]
|
||
},
|
||
{
|
||
"id": "entity-charge",
|
||
"name": "Charges",
|
||
"type": "DATA_ENTITY",
|
||
"version": "V 1.2",
|
||
"metadata": {
|
||
"fields": 8
|
||
},
|
||
"upstreamConnections": [
|
||
"fs-churn",
|
||
"is-churn",
|
||
"fs-eligible-offers",
|
||
"fs-tier-history",
|
||
"fs-monthly-deposit",
|
||
"fs-customer-segmentation",
|
||
"fs-event-history"
|
||
],
|
||
"downstreamConnections": [
|
||
"POSTGRES/EPM_CA"
|
||
]
|
||
},
|
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|
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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|
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||
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||
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||
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||
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||
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||
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|
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
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|
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|
||
{
|
||
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|
||
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|
||
"system_prompt": "# 🧠 System Prompt: Multi-Agent System for Qualification Optimization\n\n## 🎯 System Objective\n**Leverage a self-directed Multi-Agent System (MAS) to enhance qualification effectiveness and engagement by 10%.**\n\n## 🧑💼 Primary User Persona\n- **Role**: Product Manager \n- **Goals**:\n - Provide insights on tier criteria and customer behaviors.\n - Enable decisions for threshold adjustments and bundling.\n - Optimize for active engagement and reduce passive qualifications.\n - Align tiers with usage to improve retention.\n\n\n## 🧠 System Architecture & Agents\n\n### 1. Data Ingestion Agent\n- Fetches and processes data from sources.\n- **Outputs**: Cleaned datasets.\n\n### 2. Analysis Agent\n- Identifies patterns and concentrations.\n- **Outputs**: Descriptive insights.\n\n### 3. Prediction Agent\n- Forecasts impacts of changes.\n- **Outputs**: Predictive simulations.\n\n### 4. Optimization Agent\n- Suggests optimal adjustments.\n- **Outputs**: Optimized criteria.\n\n### 5. Recommendation Agent\n- Generates actionable suggestions.\n- **Outputs**: Strategic recommendations.\n\n### 6. Validation Agent\n- Validates outputs.\n- **Outputs**: Refined insights.\n\n---\n\n## 📊 Key Performance Indicators (KPIs)\n\n| KPI | Target | Measured By |\n|-----|--------|-------------|\n| **Qualification Effectiveness Uplift** | +10% | Qualification rate |\n| **Engagement Rate** | +15% | Active users/events |\n| **Passive Reduction** | -20% | Passive qualifiers |\n| **Tier Movement Rate** | +12% | Up-tier migrations |\n| **Customer Retention** | +8% | Retention rate |\n\n---\n\n## 🔍 Sample Insights\n\n### Engagement-Driven\n- \"Increasing direct deposit threshold could boost active users by 5%.\"\n\n### Optimization Levers\n- \"Bundle bill pay with auto transfer for 12% engagement lift.\"\n\n---\n\n## 🧩 Data Assets Utilized\n\n- **Customer Data**: Profiles, tenures.\n- **Transactional Data**: Events, usages.\n- **Engagement Metrics**: Active rates.\n\n---\n\n## 🔄 Decision Loop & Agent Autonomy\n\n- **Central Hub** integrates outputs.\n- Proposes plans with traceability.\n\n---\n\n## 🔐 Governance\n\n- **Human-in-the-loop** for decisions.\n- **Explainability** for recs.\n\n---\n\n## 📅 Weekly Outputs\n\n- **Summary**: Top optimizations.\n- **Impact Tracker**: Projected lifts.\n\n---\n\n## ✅ Summary\n\nEmpowers managers to optimize qualifications with data-backed insights.",
|
||
"mas": [
|
||
{
|
||
"name": "Enrich360 Offline Process",
|
||
"description": "Batch enrichment engine designed for large-scale data processing, ensuring accuracy and consistency at scale",
|
||
"updated_at": "2025-06-04T05:54:09+00:00",
|
||
"tags": [
|
||
"assistants",
|
||
"agents",
|
||
"MAS"
|
||
],
|
||
"id": "532ed470-b97d-4171-8121-07fbff800728",
|
||
"folder_id": "a2956334-e842-44ac-96aa-7190bfa5396c"
|
||
},
|
||
{
|
||
"name": "Enrich360 Interactive",
|
||
"description": "Empowers product managers with real-time, user-driven insights to make faster, data-backed decisions",
|
||
"updated_at": "2025-06-04T05:54:20+00:00",
|
||
"tags": [
|
||
"assistants",
|
||
"agents",
|
||
"MAS"
|
||
],
|
||
"id": "f8d4f2f9-5285-45b0-8eda-cd0043e230e3",
|
||
"folder_id": "a2956334-e842-44ac-96aa-7190bfa5396c"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"name": "Presentations",
|
||
"description": "Visual insights and demos showcasing Enrich360 capabilities for qualification optimization.",
|
||
"deployed_urls": [
|
||
{
|
||
"url": "https://td.demo2.epbcloud.org/FPB/dashboard/command-center/qualification-effectiveness",
|
||
"description": "This is where the Enrich360 UI is deployed. Click to open the live instance."
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"name": "Deployment",
|
||
"description": "Tools and settings to launch, monitor, and manage AI solutions in real-time environments.",
|
||
"configuration": "Kubernetes",
|
||
"version": "V 1.2",
|
||
"replicas": {
|
||
"model": 3,
|
||
"agent": 2,
|
||
"logging": 1,
|
||
"monitoring": 1
|
||
},
|
||
"yaml": "# -----------------------------------------------------\n# enrich360 AI Model Deployment for Qualification Optimization\n# -----------------------------------------------------\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: enrich360-model\n labels:\n app: enrich360\n component: model\nspec:\n replicas: 3\n selector:\n matchLabels:\n app: enrich360\n component: model\n template:\n metadata:\n labels:\n app: enrich360\n component: model\n spec:\n containers:\n - name: model-server\n image: enrich360/model-server:latest\n ports:\n - containerPort: 8000\n env:\n - name: MODEL_PATH\n value: /models/classifier-v2.pth\n - name: LOG_LEVEL\n value: info\n volumeMounts:\n - name: model-storage\n mountPath: /models\n volumes:\n - name: model-storage\n persistentVolumeClaim:\n claimName: enrich360-model-pvc\n\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: enrich360-model-service\nspec:\n selector:\n app: enrich360\n component: model\n ports:\n - port: 80\n targetPort: 8000\n type: ClusterIP\n\n---\n# -----------------------------------------------------\n# enrich360 Agent (AI Decision-Maker)\n# -----------------------------------------------------\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: enrich360-agent\n labels:\n app: enrich360\n component: agent\nspec:\n replicas: 2\n selector:\n matchLabels:\n app: enrich360\n component: agent\n template:\n metadata:\n labels:\n app: enrich360\n component: agent\n spec:\n containers:\n - name: agent-service\n image: enrich360/agent:stable\n ports:\n - containerPort: 8500\n env:\n - name: MODEL_API_URL\n value: http://enrich360-model-service/predict\n - name: AGENT_MODE\n value: production\n\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: enrich360-agent-service\nspec:\n selector:\n app: enrich360\n component: agent\n ports:\n - port: 85\n targetPort: 8500\n type: ClusterIP\n\n---\n# -----------------------------------------------------\n# enrich360 Logging Service (e.g., Fluentd, Loki)\n# -----------------------------------------------------\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: enrich360-logging\n labels:\n app: enrich360\n component: logging\nspec:\n replicas: 1\n selector:\n matchLabels:\n app: enrich360\n component: logging\n template:\n metadata:\n labels:\n app: enrich360\n component: logging\n spec:\n containers:\n - name: logger\n image: enrich360/logging:latest\n ports:\n - containerPort: 24224\n volumeMounts:\n - name: log-storage\n mountPath: /var/log/enrich360\n volumes:\n - name: log-storage\n emptyDir: {}\n\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: enrich360-logging-service\nspec:\n selector:\n app: enrich360\n component: logging\n ports:\n - port: 24224\n targetPort: 24224\n type: ClusterIP\n\n---\n# -----------------------------------------------------\n# enrich360 Monitoring Dashboard (e.g., Prometheus + Grafana)\n# -----------------------------------------------------\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: enrich360-monitoring\n labels:\n app: enrich360\n component: monitoring\nspec:\n replicas: 1\n selector:\n matchLabels:\n app: enrich360\n component: monitoring\n template:\n metadata:\n labels:\n app: enrich360\n component: monitoring\n spec:\n containers:\n - name: prometheus\n image: prom/prometheus:latest\n ports:\n - containerPort: 9090\n volumeMounts:\n - name: prom-config\n mountPath: /etc/prometheus\n volumes:\n - name: prom-config\n configMap:\n name: enrich360-prometheus-config\n\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: enrich360-monitoring-service\nspec:\n selector:\n app: enrich360\n component: monitoring\n ports:\n - port: 90\n targetPort: 9090\n type: ClusterIP\n\n---\n# -----------------------------------------------------\n# Persistent Volume Claim for Model Storage\n# -----------------------------------------------------\napiVersion: v1\nkind: PersistentVolumeClaim\nmetadata:\n name: enrich360-model-pvc\nspec:\n accessModes:\n - ReadWriteOnce\n resources:\n requests:\n storage: 2Gi\n\n---\n# -----------------------------------------------------\n# ConfigMap for Prometheus Configuration (Example)\n# -----------------------------------------------------\napiVersion: v1\nkind: ConfigMap\nmetadata:\n name: enrich360-prometheus-config\ndata:\n prometheus.yml: |\n global:\n scrape_interval: 15s\n scrape_configs:\n - job_name: 'enrich360'\n static_configs:\n - targets: ['enrich360-model-service:80', 'enrich360-agent-service:85']",
|
||
"services": [
|
||
"enrich360-model-service",
|
||
"enrich360-agent-service",
|
||
"enrich360-logging-service",
|
||
"enrich360-monitoring-service"
|
||
],
|
||
"endpoints": [
|
||
{
|
||
"name": "Get Qualification Optimizer",
|
||
"description": "AI Agent",
|
||
"url": "/ai-agent",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Tier Insights",
|
||
"description": "Dataset",
|
||
"url": "/dataset",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Engagement Metrics",
|
||
"description": "Dashboard",
|
||
"url": "/dashboard",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Qualification Management Insights",
|
||
"description": "ML Model",
|
||
"url": "/ml-model",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Market Insights",
|
||
"description": "Gen AI Model",
|
||
"url": "/gen-ai-model",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Customer Churning",
|
||
"description": "ML Model",
|
||
"url": "/ml-model",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Competitor Watch",
|
||
"description": "Gen AI Model",
|
||
"url": "/gen-ai-model",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Get Suggested Questions",
|
||
"description": "Gen AI Model",
|
||
"url": "/gen-ai-model",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "LYZA Enrich360 MAS",
|
||
"description": "Enrich360 MAS",
|
||
"url": "/enrich360-mas",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
},
|
||
{
|
||
"name": "Authentication Service",
|
||
"description": "Handles user authentication and authorization",
|
||
"url": "/auth",
|
||
"version": "1.0.0",
|
||
"status": "Active"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"name": "KPI",
|
||
"description": "Dashboard tracking key performance indicators to evaluate AI application effectiveness.",
|
||
"business": [
|
||
{
|
||
"name": "Engagement",
|
||
"description": "Overall Engagement Targets",
|
||
"id": "kpi-business-001",
|
||
"url": "/kpis/engagement",
|
||
"version": "V 1.2",
|
||
"status": "Improving",
|
||
"trend": "+5%",
|
||
"target": "50%"
|
||
},
|
||
{
|
||
"name": "Qualification Rate",
|
||
"description": "Qualification Effectiveness",
|
||
"id": "kpi-business-002",
|
||
"url": "/kpis/qualification",
|
||
"version": "V 1.2",
|
||
"status": "Improving",
|
||
"trend": "+8%",
|
||
"target": "60%"
|
||
},
|
||
{
|
||
"name": "Active Users",
|
||
"description": "Active User Growth",
|
||
"id": "kpi-business-003",
|
||
"url": "/kpis/active-users",
|
||
"version": "V 1.2",
|
||
"status": "Stable",
|
||
"trend": "+3%",
|
||
"target": "75%"
|
||
},
|
||
{
|
||
"name": "Churn Rate",
|
||
"description": "Target churn rate of customers",
|
||
"id": "kpi-business-004",
|
||
"url": "/kpis/churn-rate",
|
||
"version": "V 1.2",
|
||
"status": "Improving",
|
||
"trend": "+2%",
|
||
"target": "12.5%"
|
||
},
|
||
{
|
||
"name": "Product Usage",
|
||
"description": "Transactions",
|
||
"id": "kpi-business-005",
|
||
"url": "/kpis/product-usage",
|
||
"version": "V 1.2",
|
||
"status": "Declining",
|
||
"trend": "-$2.35M",
|
||
"target": "24.5M"
|
||
},
|
||
{
|
||
"name": "Waivers",
|
||
"description": "Product Waivers",
|
||
"id": "kpi-business-006",
|
||
"url": "/kpis/waivers",
|
||
"version": "V 1.2",
|
||
"status": "Improving",
|
||
"trend": "+11.3%",
|
||
"target": "$9.6M"
|
||
}
|
||
],
|
||
"technical": [
|
||
{
|
||
"name": "Processing Efficiency",
|
||
"description": "Current processing efficiency rate",
|
||
"id": "kpi-technical-001",
|
||
"url": "/kpis/processing-efficiency",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"current": "98%",
|
||
"target": "95%",
|
||
"trend": "+3% from last month"
|
||
},
|
||
{
|
||
"name": "Error Rate",
|
||
"description": "System error occurrence rate",
|
||
"id": "kpi-technical-002",
|
||
"url": "/kpis/error-rate",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"current": "1.2%",
|
||
"target": "<2%",
|
||
"trend": "-0.5% from last month"
|
||
},
|
||
{
|
||
"name": "Response Time",
|
||
"description": "Average API response time",
|
||
"id": "kpi-technical-003",
|
||
"url": "/kpis/response-time",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"current": "150ms",
|
||
"target": "<200ms",
|
||
"trend": "-50ms from last month"
|
||
},
|
||
{
|
||
"name": "Uptime",
|
||
"description": "System availability percentage",
|
||
"id": "kpi-technical-004",
|
||
"url": "/kpis/uptime",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"current": "99.9%",
|
||
"target": "99.9%",
|
||
"trend": "+0.1% from last month"
|
||
},
|
||
{
|
||
"name": "Data Accuracy",
|
||
"description": "Data validation success rate",
|
||
"id": "kpi-technical-005",
|
||
"url": "/kpis/data-accuracy",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"current": "99.5%",
|
||
"target": "99%",
|
||
"trend": "+0.5% from last month"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"name": "Reports",
|
||
"description": "Generated summaries and logs detailing system performance and agent behavior.",
|
||
"reports": [
|
||
{
|
||
"name": "Effectiveness Report",
|
||
"description": "Detailed analysis of qualification effectiveness metrics",
|
||
"id": "report-effectiveness-001",
|
||
"url": "/reports/effectiveness",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"last_updated": "Today"
|
||
},
|
||
{
|
||
"name": "Usage Analytics",
|
||
"description": "User engagement and system utilization statistics",
|
||
"id": "report-usage-analytics-001",
|
||
"url": "/reports/usage-analytics",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"last_updated": "Today"
|
||
},
|
||
{
|
||
"name": "Error Log Report",
|
||
"description": "Comprehensive error tracking and resolution status",
|
||
"id": "report-error-log-001",
|
||
"url": "/reports/error-logs",
|
||
"version": "V 1.2",
|
||
"status": "Active",
|
||
"last_updated": "Today"
|
||
}
|
||
]
|
||
}
|
||
]
|
||
} |