diff --git a/__intelliarcs/enrich-360.json b/__intelliarcs/enrich-360.json deleted file mode 100644 index a8c8eb4..0000000 --- a/__intelliarcs/enrich-360.json +++ /dev/null @@ -1,1282 +0,0 @@ -{ - "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": [ - 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"name": "Product Recommendation Insights data", - "type": "INSIGHTS_STORE", - "version": "V 1.2", - "metadata": { - "fields": 8, - "workflows": 2, - "workflows_data": [{"name": "Balance_Prediction_EPM_CAA_Feature_Store"}, {"name": "Balance_Prediction_EPM_CAA_Feature_Store"}] - }, - "upstreamConnections": [], - "downstreamConnections": [] - }, - { - "id": "is-qualification", - "name": "Qualification Insights Store", - "type": "INSIGHTS_STORE", - "version": "V 1.2", - "metadata": { - "fields": 9, - "workflows": 2, - "workflows_data": [{"name": "Balance_Prediction_EPM_CAA_Feature_Store"}, {"name": "Balance_Prediction_EPM_CAA_Feature_Store"}] - }, - "upstreamConnections": [ - "model-qualification-effectiveness" - ], - "downstreamConnections": [ - "entity-customer", - "entity-eligible-transactions-events", - "entity-recommendation" - ] - }, - { - "id": "is-engagement", - "name": "Engagement Insights Store", - "type": "INSIGHTS_STORE", - "version": "V 1.2", - "metadata": { - "fields": 7, - "workflows": 1, - "workflows_data": [{"name": "Balance_Prediction_EPM_CAA_Feature_Store"}] - }, - "upstreamConnections": [ - "model-time-series" - ], - "downstreamConnections": [ - "entity-tier" - ] - }, - { - "id": "is-tier", - "name": "Tier Insights store", - "type": "INSIGHTS_STORE", - "version": "V 1.2", - "metadata": { - "fields": 8, - "workflows": 2, - "workflows_data": [{"name": "Balance_Prediction_EPM_CAA_Feature_Store"}, {"name": "Balance_Prediction_EPM_CAA_Feature_Store"}] - }, - "upstreamConnections": [ - "model-tier-movement" - ], - "downstreamConnections": [] - } - ], - "dataSources": [ - { - "id": "ds-systematics", - "name": "Systematics Banking Core", - "type": "DATA_SOURCE", - "version": "V 1.2", - "metadata": { - "files": 12, - "fields": 45 - }, - "upstreamConnections": [], - "downstreamConnections": [] - }, - { - "id": "POSTGRES/EPM_CA", - "name": "Enterprise Profit Maximization", - "type": "DATA_SOURCE", - "version": "V 1.2", - "metadata": { - "files": 8, - "fields": 32 - }, - "upstreamConnections": [ - "entity-customer", - "entity-account", - "entity-transactions", - "entity-balances", - "entity-products", - "entity-charge", - "POSTGRES/EPM_CA/folder/pricelist" - ], - "downstreamConnections": [] - } - ] - } - }, - { - "name": "LYZA", - "description": "Multi-Agent System (MAS) designed to empower users to create AI application", - "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" - } - ] - } - ] -} \ No newline at end of file