From 7083de56ec4156c2a0caa171499857dda6036ef4 Mon Sep 17 00:00:00 2001 From: gitea_admin Date: Thu, 23 Jul 2026 09:56:49 +0000 Subject: [PATCH] Upload files to "__intelliarcs" --- __intelliarcs/enrich-360.json | 1282 +++++++++++++++++++++++++++++ __intelliarcs/exceptions-360.json | 830 +++++++++++++++++++ __intelliarcs/experience-360.json | 829 +++++++++++++++++++ __intelliarcs/meta.json | 26 + 4 files changed, 2967 insertions(+) create mode 100644 __intelliarcs/enrich-360.json create mode 100644 __intelliarcs/exceptions-360.json create mode 100644 __intelliarcs/experience-360.json create mode 100644 __intelliarcs/meta.json diff --git a/__intelliarcs/enrich-360.json b/__intelliarcs/enrich-360.json new file mode 100644 index 0000000..a8c8eb4 --- /dev/null +++ b/__intelliarcs/enrich-360.json @@ -0,0 +1,1282 @@ +{ + "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 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"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## πŸ§‘β€πŸ’Ό 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## πŸ“Š 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## πŸ” 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## πŸ”„ Decision Loop & Agent Autonomy\n\n- **Central Hub** integrates outputs.\n- Proposes plans with traceability.\n\n---\n\n## πŸ” Governance\n\n- **Human-in-the-loop** for decisions.\n- **Explainability** for recs.\n\n---\n\n## πŸ“… Weekly Outputs\n\n- **Summary**: Top optimizations.\n- **Impact Tracker**: Projected lifts.\n\n---\n\n## βœ… 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 diff --git a/__intelliarcs/exceptions-360.json b/__intelliarcs/exceptions-360.json new file mode 100644 index 0000000..01fbd74 --- /dev/null +++ b/__intelliarcs/exceptions-360.json @@ -0,0 +1,830 @@ +{ + "id": "experience-360", + "name": "Exceptions 360", + "description": "Revolutionize customer engagement with Experience360 a powerful AI-driven platform designed for both agents and customers.", + "status": "87% Complete", + "statusType": "progress", + "tabs": [ + { + "name": "Overview", + "description": "Revolutionize customer engagement with Experience360 a powerful AI-driven platform designed for both agents and customers.", + "goals": ["Enhance the profitability of the target product by 6%"], + "objective": "Leverage a self-directed Multi Agent System to achieve a 6% increase in target product profitability", + "roles": [ + { + "name": "Product Manager", + "description": "" + } + ], + "benefits": [ + "Deliver proactive insights to help product managers anticipate market trends and customer needs", + "Enable data-driven decision-making that directly supports revenue growth and strategic product development", + "Identify opportunities to boost profitability through optimized pricing, feature prioritization, and resource allocation", + "Strengthen customer loyalty by aligning product decisions with user behavior and engagement data" + ] + }, + { + "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-churn", + "agent-recommendation", + "agent-sentiment", + "agent-chart-generation", + "agent-competitor-insights", + "agent-price-optimization", + "agent-product-intelligence", + "agent-usage-analyzer", + "agent-market-insights", + "agent-similar-accounts", + "340e80ac-f9ce-402f-a9c6-06370cf3f070" + ] + } + ], + "agents": [ + { + "id": "agent-churn", + "name": "Churn Management Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Manages customer churn prevention" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": ["model-churn", "model-churn-mitigation"] + }, + { + "id": "agent-recommendation", + "name": "Product Recommendation Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Suggests products by customer" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": ["model-recommendation"] + }, + { + "id": "agent-sentiment", + "name": "Customer Sentiment Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Analyzes customer sentiment and feedback" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": ["model-sentiment"] + }, + { + "id": "agent-chart-generation", + "name": "Chart Generation Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Creates charts and graphs from data" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-competitor-insights", + "name": "Competitor Insights Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights on product and market" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-price-optimization", + "name": "Price Optimization Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for customer" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-product-intelligence", + "name": "Product Intelligence Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for product" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-usage-analyzer", + "name": "Usage Analyzer Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for user" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-market-insights", + "name": "Market Insights Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for industry and market" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-similar-accounts", + "name": "Similar Accounts", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Finds accounts for users" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "340e80ac-f9ce-402f-a9c6-06370cf3f070", + "name": "EPM Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers data analytics and digital" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + } + ], + "models": [ + { + "id": "model-churn", + "name": "Likelihood of Churn Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": ["agent-churn"], + "downstreamConnections": ["fs-churn", "is-churn"] + }, + { + "id": "model-recommendation", + "name": "Product Recommendation Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 12, + "size": "2 GB" + }, + "upstreamConnections": ["agent-recommendation"], + "downstreamConnections": [] + }, + { + "id": "model-sentiment", + "name": "Sentiment Analysis Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 6, + "size": "1 GB" + }, + "upstreamConnections": ["agent-sentiment"], + "downstreamConnections": [] + }, + { + "id": "model-competitor-insights", + "name": "Competitor Insights Agent", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-price-optimization", + "name": "Price Optimization Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-product-intelligence", + "name": "Product Intelligence Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-usage-classification", + "name": "Usage Classification Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-similar-accounts", + "name": "Similar Accounts Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-usage-forecasting", + "name": "Usage Forecasting Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "232", + "name": "Churn Mitigation Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": ["agent-churn"], + "downstreamConnections": ["fs-churn", "is-churn"] + } + + ], + "tools": [ + { + "id": "tool-batch", + "name": "Batch job", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 4 + }, + "upstreamConnections": ["model-churn"], + "downstreamConnections": ["fs-churn"] + }, + { + "id": "tool-stream", + "name": "Stream", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 2 + }, + "upstreamConnections": ["model-churn"], + "downstreamConnections": ["is-churn"] + }, + { + "id": "tool-chart-generation", + "name": "Chart Generation Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 4 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-news-mcf", + "name": "News MCF tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-weather-mcf", + "name": "Weather MCF tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 3 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-web-search", + "name": "Web Search Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-epm-text-to-sql", + "name": "EPM Text-to-SQL Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 1 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-epm-mcf", + "name": "EPM MCF Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 1 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ], + "dataEntities": [ + { + "id": "entity-customer", + "name": "Customer", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 15 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + }, + { + "id": "entity-invoices", + "name": "Invoices", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 12 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + }, + { + "id": "entity-transactions", + "name": "Transactions", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 18 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + }, + { + "id": "entity-products", + "name": "Products", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 8 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + } + ], + "featureStores": [ + { + "id": "fs-churn", + "name": "Churn feature Store", + "type": "FEATURE_STORE", + "version": "V 1.2", + "metadata": { + "fields": 8, + "workflows": 1 + }, + "upstreamConnections": ["model-churn", "model-churn-mitigation"], + "downstreamConnections": [ + "entity-customer", + "entity-invoices", + "entity-transactions", + "entity-products" + ] + }, + { + "id": "fs-product-recommendation", + "name": "Product Recommendation Feature Store", + "type": "FEATURE_STORE", + "version": "V 1.2", + "metadata": { + "fields": 10, + "workflows": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ], + "insightStores": [ + { + "id": "is-churn", + "name": "Churn Insights Store", + "type": "INSIGHTS_STORE", + "version": "V 1.2", + "metadata": { + "fields": 8, + "workflows": 1 + }, + "upstreamConnections": ["model-churn", "model-churn-mitigation"], + "downstreamConnections": [ + "entity-customer", + "entity-invoices", + "entity-transactions", + "entity-products" + ] + }, + { + "id": "insight-churn-data", + "name": "Churn Insights Data", + "type": "INSIGHTS_STORE", + "version": "V 1.2", + "metadata": { + "fields": 6, + "workflows": 1 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "insight-product-recommendation", + "name": "Product Recommendation Insights data", + "type": "INSIGHTS_STORE", + "version": "V 1.2", + "metadata": { + "fields": 8, + "workflows": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ], + "dataSources": [ + { + "id": "ds-hogan", + "name": "Hogan Banking Core", + "type": "DATA_SOURCE", + "version": "V 1.2", + "metadata": { + "files": 12, + "fields": 45 + }, + "upstreamConnections": [ + "entity-customer", + "entity-invoices", + "entity-transactions", + "entity-products" + ], + "downstreamConnections": [] + }, + { + "id": "ds-salesforce", + "name": "Salesforce CRM", + "type": "DATA_SOURCE", + "version": "V 1.2", + "metadata": { + "files": 8, + "fields": 32 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ] + } + }, + { + "name": "LYZA", + "description": "Multi-Agent System (MAS) designed to empower users to create AI application", + "system_prompt": "# 🧠 System Prompt: Multi-Agent System for Profitability Optimization\n\n## 🎯 System Objective\n**Leverage a self-directed Multi-Agent System (MAS) to enhance the profitability of the target product by 6%.**\n\n## πŸ§‘β€πŸ’Ό Primary User Persona\n- **Role**: Product Manager \n- **Goals**:\n - Deliver proactive insights on market trends and customer needs.\n - Enable data-driven decision-making for revenue and strategic development.\n - Identify opportunities through pricing, feature prioritization, and resource allocation.\n - Align product direction with user behavior to boost loyalty and retention.\n\n\n## 🧠 System Architecture & Agents\n\n### 1. Market Trends Agent\n- Scrapes and synthesizes industry news, competitor moves, and consumer behavior.\n- Forecasts market demand shifts.\n- **Outputs**: Trend dashboards, early warnings, opportunity maps.\n\n### 2. Customer Intelligence Agent\n- Analyzes interaction logs, reviews, NPS, and churn indicators.\n- Segments users by behavior, sentiment, and value.\n- **Outputs**: Loyalty drivers, friction points, feature adoption trends.\n\n### 3. Pricing & Profitability Agent\n- Simulates pricing and bundling strategies using internal and competitive data.\n- Optimizes for revenue per user, margin, and LTV/CAC.\n- **Outputs**: Dynamic pricing recs, test strategies.\n\n### 4. Feature Prioritization Agent\n- Evaluates feature ROI using A/B test data, analytics, and requests.\n- Prioritizes by impact, feasibility, alignment.\n- **Outputs**: Roadmap priorities with ROI scoring.\n\n### 5. Strategic Alignment Agent\n- Maps recommendations to business OKRs and product KPIs.\n- Aligns output with executive vision.\n- **Outputs**: OKR scorecards, strategic impact alerts.\n\n---\n\n## πŸ“Š Key Performance Indicators (KPIs)\n\n| KPI | Target | Measured By |\n|-----|--------|-------------|\n| **Product Profitability Uplift** | +6% | (Revenue - COGS)/Revenue |\n| **Customer Lifetime Value (LTV)** | +10% | Avg. revenue per customer lifecycle |\n| **Churn Rate** | -15% | % of users leaving per month |\n| **Feature Adoption Rate** | +20% | % of users using new features |\n| **ARPU (Avg. Revenue/User)** | +5% | Revenue Γ· Active Users |\n| **Time-to-Insight** | -30% | Data β†’ Action latency |\n| **Insight Utilization Rate** | +40% | % of insights influencing actions |\n\n---\n\n## πŸ” Sample Insights\n\n### Market-Driven\n- \"Gen Z shift to mobile-first productivity tools = 12% drop in desktop use.\"\n- \"Competitor pricing shift caused 8% churn in overlapping user base.\"\n\n### Customer Behavior\n- \"Low onboarding completion = 3x higher churn.\"\n- \"Power users love Feature X; UI promotion could lift adoption by 15%.\"\n\n### Profitability Levers\n- \"Feature A + B bundle β†’ +9% profit in Tier 2 users.\"\n- \"Proactive support bot reduces per-user cost β†’ +2% margin.\"\n\n### Feature ROI\n- \"Feature C = $480K/year projected uplift.\"\n- \"Feature B = high dev cost, <1% revenue impact β€” deprioritize.\"\n\n### Strategic Forecasts\n- \"Entering Vertical Y in 6 months β†’ +15% LTV based on market signals.\"\n\n---\n\n## 🧩 Data Assets Utilized\n\n- **Product Analytics**: Mixpanel, Amplitude, GA\n- **CRM & Sales**: Salesforce, HubSpot\n- **Customer Feedback**: NPS, Zendesk, UserVoice\n- **Behavioral Tools**: Segment, FullStory, Heap\n- **Financial Systems**: ERP, P&L Data\n- **Competitive Data**: SimilarWeb, AppAnnie\n- **Custom Data**: Proprietary usage and support metrics\n\n---\n\n## πŸ”„ Decision Loop & Agent Autonomy\n\n- **Central Coordination Hub** integrates agent output.\n- Resolves conflicts, ranks ideas by profitability impact.\n- Proposes action plans with data traceability.\n- **Escalation** to Product Manager for high-impact decisions.\n\n---\n\n## πŸ” Governance, Control & Guardrails\n\n- **Human-in-the-loop** for major product/pricing decisions.\n- **Bias detection** on predictive models.\n- **Explainability** for all recommendations (with data trace).\n- **Audit logs** for decision transparency.\n\n---\n\n## πŸ“… Weekly Outputs\n\n- **Executive Summary**: Top 5 profitability drivers.\n- **Feature ROI Tracker**: Updated roadmap impacts.\n- **Customer Health Report**: Segments at risk/opportunity.\n- **Pricing Simulations**: Scenarios with projected profit impact.\n- **Agent Confidence Scores**: Trust levels per suggestion.\n\n---\n\n## βœ… Summary\n\nA self-directed MAS empowers the Product Manager to:\n- Predict market trends.\n- Act on user needs.\n- Make smarter roadmap and pricing decisions.\n- Achieve a sustained 6%+ profitability uplift with explainable, 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 Experience 360 capabilities.", + "deployed_urls": [ + { + "url": "/intarc/dashboard", + "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 solution 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\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 Revenue Optimizer", + "description": "AI Agent", + "url": "/ai-agent", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Alerts", + "description": "Dataset", + "url": "/dataset", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Product Metrics", + "description": "Dashboard", + "url": "/dashboard", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Churn Management Insights", + "description": "ML Model", + "url": "/ml-model", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Prospect Identification Insights", + "description": "Model", + "url": "/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 Sentiment", + "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": "Revenue", + "description": "Overall Revenue Targets", + "id": "kpi-business-001", + "url": "/kpis/revenue", + "version": "V 1.2", + "status": "Declining", + "trend": "-4%", + "target": "$100.3M" + }, + { + "name": "Profitability", + "description": "Product Profitability", + "id": "kpi-business-002", + "url": "/kpis/profitability", + "version": "V 1.2", + "status": "Declining", + "trend": "-6%", + "target": "45%" + }, + { + "name": "Cost to Serve", + "description": "Cost to Serve my product", + "id": "kpi-business-003", + "url": "/kpis/cost-to-serve", + "version": "V 1.2", + "status": "Declining", + "trend": "-2.5%", + "target": "$18.2M" + }, + { + "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": "Performance Report", + "description": "Detailed analysis of system performance metrics", + "id": "report-performance-001", + "url": "/reports/performance", + "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" + } + ] + } + ] +} diff --git a/__intelliarcs/experience-360.json b/__intelliarcs/experience-360.json new file mode 100644 index 0000000..220dc9d --- /dev/null +++ b/__intelliarcs/experience-360.json @@ -0,0 +1,829 @@ +{ + "id": "experience-360", + "name": "Experience 360", + "description": "Revolutionize customer engagement with Experience360 a powerful AI-driven platform designed for both agents and customers.", + "status": "87% Complete", + "statusType": "progress", + "tabs": [ + { + "name": "Overview", + "description": "Revolutionize customer engagement with Experience360 a powerful AI-driven platform designed for both agents and customers.", + "goals": ["Enhance the profitability of the target product by 6%"], + "objective": "Leverage a self-directed Multi Agent System to achieve a 6% increase in target product profitability", + "roles": [ + { + "name": "Product Manager", + "description": "" + } + ], + "benefits": [ + "Deliver proactive insights to help product managers anticipate market trends and customer needs", + "Enable data-driven decision-making that directly supports revenue growth and strategic product development", + "Identify opportunities to boost profitability through optimized pricing, feature prioritization, and resource allocation", + "Strengthen customer loyalty by aligning product decisions with user behavior and engagement data" + ] + }, + { + "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-churn", + "agent-recommendation", + "agent-sentiment", + "agent-chart-generation", + "agent-competitor-insights", + "agent-price-optimization", + "agent-product-intelligence", + "agent-usage-analyzer", + "agent-market-insights", + "agent-similar-accounts", + "agent-epm" + ] + } + ], + "agents": [ + { + "id": "agent-churn", + "name": "Churn Management Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Manages customer churn prevention" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": ["model-churn", "model-churn-mitigation"] + }, + { + "id": "agent-recommendation", + "name": "Product Recommendation Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Suggests products by customer" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": ["model-recommendation"] + }, + { + "id": "agent-sentiment", + "name": "Customer Sentiment Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Analyzes customer sentiment and feedback" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": ["model-sentiment"] + }, + { + "id": "agent-chart-generation", + "name": "Chart Generation Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Creates charts and graphs from data" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-competitor-insights", + "name": "Competitor Insights Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights on product and market" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-price-optimization", + "name": "Price Optimization Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for customer" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-product-intelligence", + "name": "Product Intelligence Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for product" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-usage-analyzer", + "name": "Usage Analyzer Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for user" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-market-insights", + "name": "Market Insights Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers insights for industry and market" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-similar-accounts", + "name": "Similar Accounts", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Finds accounts for users" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + }, + { + "id": "agent-epm", + "name": "EPM Agent", + "type": "AGENT", + "version": "V 1.2", + "metadata": { + "description": "Offers data analytics and digital" + }, + "upstreamConnections": ["orchestrator-1"], + "downstreamConnections": [] + } + ], + "models": [ + { + "id": "model-churn", + "name": "Likelihood of Churn Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": ["agent-churn"], + "downstreamConnections": ["fs-churn", "is-churn"] + }, + { + "id": "model-recommendation", + "name": "Product Recommendation Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 12, + "size": "2 GB" + }, + "upstreamConnections": ["agent-recommendation"], + "downstreamConnections": [] + }, + { + "id": "model-sentiment", + "name": "Sentiment Analysis Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 6, + "size": "1 GB" + }, + "upstreamConnections": ["agent-sentiment"], + "downstreamConnections": [] + }, + { + "id": "model-competitor-insights", + "name": "Competitor Insights Agent", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-price-optimization", + "name": "Price Optimization Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-product-intelligence", + "name": "Product Intelligence Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-usage-classification", + "name": "Usage Classification Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-similar-accounts", + "name": "Similar Accounts Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-usage-forecasting", + "name": "Usage Forecasting Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "model-churn-mitigation", + "name": "Churn Mitigation Model", + "type": "MODEL", + "version": "V 1.2", + "metadata": { + "parameters": 8, + "size": "1 GB" + }, + "upstreamConnections": ["agent-churn"], + "downstreamConnections": ["fs-churn", "is-churn"] + } + ], + "tools": [ + { + "id": "tool-batch", + "name": "Batch job", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 4 + }, + "upstreamConnections": ["model-churn"], + "downstreamConnections": ["fs-churn"] + }, + { + "id": "tool-stream", + "name": "Stream", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 2 + }, + "upstreamConnections": ["model-churn"], + "downstreamConnections": ["is-churn"] + }, + { + "id": "tool-chart-generation", + "name": "Chart Generation Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 4 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-news-mcf", + "name": "News MCF tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-weather-mcf", + "name": "Weather MCF tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 3 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-web-search", + "name": "Web Search Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-epm-text-to-sql", + "name": "EPM Text-to-SQL Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 1 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "tool-epm-mcf", + "name": "EPM MCF Tool", + "type": "TOOL", + "version": "V 1.2", + "metadata": { + "interfaces": 1 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ], + "dataEntities": [ + { + "id": "entity-customer", + "name": "Customer", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 15 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + }, + { + "id": "entity-invoices", + "name": "Invoices", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 12 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + }, + { + "id": "entity-transactions", + "name": "Transactions", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 18 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + }, + { + "id": "entity-products", + "name": "Products", + "type": "DATA_ENTITY", + "version": "V 1.2", + "metadata": { + "fields": 8 + }, + "upstreamConnections": ["fs-churn", "is-churn"], + "downstreamConnections": ["ds-hogan"] + } + ], + "featureStores": [ + { + "id": "fs-churn", + "name": "Churn feature Store", + "type": "FEATURE_STORE", + "version": "V 1.2", + "metadata": { + "fields": 8, + "workflows": 1 + }, + "upstreamConnections": ["model-churn", "model-churn-mitigation"], + "downstreamConnections": [ + "entity-customer", + "entity-invoices", + "entity-transactions", + "entity-products" + ] + }, + { + "id": "fs-product-recommendation", + "name": "Product Recommendation Feature Store", + "type": "FEATURE_STORE", + "version": "V 1.2", + "metadata": { + "fields": 10, + "workflows": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ], + "insightStores": [ + { + "id": "is-churn", + "name": "Churn Insights Store", + "type": "INSIGHTS_STORE", + "version": "V 1.2", + "metadata": { + "fields": 8, + "workflows": 1 + }, + "upstreamConnections": ["model-churn", "model-churn-mitigation"], + "downstreamConnections": [ + "entity-customer", + "entity-invoices", + "entity-transactions", + "entity-products" + ] + }, + { + "id": "insight-churn-data", + "name": "Churn Insights Data", + "type": "INSIGHTS_STORE", + "version": "V 1.2", + "metadata": { + "fields": 6, + "workflows": 1 + }, + "upstreamConnections": [], + "downstreamConnections": [] + }, + { + "id": "insight-product-recommendation", + "name": "Product Recommendation Insights data", + "type": "INSIGHTS_STORE", + "version": "V 1.2", + "metadata": { + "fields": 8, + "workflows": 2 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ], + "dataSources": [ + { + "id": "ds-hogan", + "name": "Hogan Banking Core", + "type": "DATA_SOURCE", + "version": "V 1.2", + "metadata": { + "files": 12, + "fields": 45 + }, + "upstreamConnections": [ + "entity-customer", + "entity-invoices", + "entity-transactions", + "entity-products" + ], + "downstreamConnections": [] + }, + { + "id": "ds-salesforce", + "name": "Salesforce CRM", + "type": "DATA_SOURCE", + "version": "V 1.2", + "metadata": { + "files": 8, + "fields": 32 + }, + "upstreamConnections": [], + "downstreamConnections": [] + } + ] + } + }, + { + "name": "LYZA", + "description": "Multi-Agent System (MAS) designed to empower users to create AI application", + "system_prompt": "# 🧠 System Prompt: Multi-Agent System for Profitability Optimization\n\n## 🎯 System Objective\n**Leverage a self-directed Multi-Agent System (MAS) to enhance the profitability of the target product by 6%.**\n\n## πŸ§‘β€πŸ’Ό Primary User Persona\n- **Role**: Product Manager \n- **Goals**:\n - Deliver proactive insights on market trends and customer needs.\n - Enable data-driven decision-making for revenue and strategic development.\n - Identify opportunities through pricing, feature prioritization, and resource allocation.\n - Align product direction with user behavior to boost loyalty and retention.\n\n\n## 🧠 System Architecture & Agents\n\n### 1. Market Trends Agent\n- Scrapes and synthesizes industry news, competitor moves, and consumer behavior.\n- Forecasts market demand shifts.\n- **Outputs**: Trend dashboards, early warnings, opportunity maps.\n\n### 2. Customer Intelligence Agent\n- Analyzes interaction logs, reviews, NPS, and churn indicators.\n- Segments users by behavior, sentiment, and value.\n- **Outputs**: Loyalty drivers, friction points, feature adoption trends.\n\n### 3. Pricing & Profitability Agent\n- Simulates pricing and bundling strategies using internal and competitive data.\n- Optimizes for revenue per user, margin, and LTV/CAC.\n- **Outputs**: Dynamic pricing recs, test strategies.\n\n### 4. Feature Prioritization Agent\n- Evaluates feature ROI using A/B test data, analytics, and requests.\n- Prioritizes by impact, feasibility, alignment.\n- **Outputs**: Roadmap priorities with ROI scoring.\n\n### 5. Strategic Alignment Agent\n- Maps recommendations to business OKRs and product KPIs.\n- Aligns output with executive vision.\n- **Outputs**: OKR scorecards, strategic impact alerts.\n\n---\n\n## πŸ“Š Key Performance Indicators (KPIs)\n\n| KPI | Target | Measured By |\n|-----|--------|-------------|\n| **Product Profitability Uplift** | +6% | (Revenue - COGS)/Revenue |\n| **Customer Lifetime Value (LTV)** | +10% | Avg. revenue per customer lifecycle |\n| **Churn Rate** | -15% | % of users leaving per month |\n| **Feature Adoption Rate** | +20% | % of users using new features |\n| **ARPU (Avg. Revenue/User)** | +5% | Revenue Γ· Active Users |\n| **Time-to-Insight** | -30% | Data β†’ Action latency |\n| **Insight Utilization Rate** | +40% | % of insights influencing actions |\n\n---\n\n## πŸ” Sample Insights\n\n### Market-Driven\n- \"Gen Z shift to mobile-first productivity tools = 12% drop in desktop use.\"\n- \"Competitor pricing shift caused 8% churn in overlapping user base.\"\n\n### Customer Behavior\n- \"Low onboarding completion = 3x higher churn.\"\n- \"Power users love Feature X; UI promotion could lift adoption by 15%.\"\n\n### Profitability Levers\n- \"Feature A + B bundle β†’ +9% profit in Tier 2 users.\"\n- \"Proactive support bot reduces per-user cost β†’ +2% margin.\"\n\n### Feature ROI\n- \"Feature C = $480K/year projected uplift.\"\n- \"Feature B = high dev cost, <1% revenue impact β€” deprioritize.\"\n\n### Strategic Forecasts\n- \"Entering Vertical Y in 6 months β†’ +15% LTV based on market signals.\"\n\n---\n\n## 🧩 Data Assets Utilized\n\n- **Product Analytics**: Mixpanel, Amplitude, GA\n- **CRM & Sales**: Salesforce, HubSpot\n- **Customer Feedback**: NPS, Zendesk, UserVoice\n- **Behavioral Tools**: Segment, FullStory, Heap\n- **Financial Systems**: ERP, P&L Data\n- **Competitive Data**: SimilarWeb, AppAnnie\n- **Custom Data**: Proprietary usage and support metrics\n\n---\n\n## πŸ”„ Decision Loop & Agent Autonomy\n\n- **Central Coordination Hub** integrates agent output.\n- Resolves conflicts, ranks ideas by profitability impact.\n- Proposes action plans with data traceability.\n- **Escalation** to Product Manager for high-impact decisions.\n\n---\n\n## πŸ” Governance, Control & Guardrails\n\n- **Human-in-the-loop** for major product/pricing decisions.\n- **Bias detection** on predictive models.\n- **Explainability** for all recommendations (with data trace).\n- **Audit logs** for decision transparency.\n\n---\n\n## πŸ“… Weekly Outputs\n\n- **Executive Summary**: Top 5 profitability drivers.\n- **Feature ROI Tracker**: Updated roadmap impacts.\n- **Customer Health Report**: Segments at risk/opportunity.\n- **Pricing Simulations**: Scenarios with projected profit impact.\n- **Agent Confidence Scores**: Trust levels per suggestion.\n\n---\n\n## βœ… Summary\n\nA self-directed MAS empowers the Product Manager to:\n- Predict market trends.\n- Act on user needs.\n- Make smarter roadmap and pricing decisions.\n- Achieve a sustained 6%+ profitability uplift with explainable, 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 Experience 360 capabilities.", + "deployed_urls": [ + { + "url": "/intarc/dashboard", + "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 solution 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\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 Revenue Optimizer", + "description": "AI Agent", + "url": "/ai-agent", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Alerts", + "description": "Dataset", + "url": "/dataset", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Product Metrics", + "description": "Dashboard", + "url": "/dashboard", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Churn Management Insights", + "description": "ML Model", + "url": "/ml-model", + "version": "1.0.0", + "status": "Active" + }, + { + "name": "Get Prospect Identification Insights", + "description": "Model", + "url": "/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 Sentiment", + "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": "Revenue", + "description": "Overall Revenue Targets", + "id": "kpi-business-001", + "url": "/kpis/revenue", + "version": "V 1.2", + "status": "Declining", + "trend": "-4%", + "target": "$100.3M" + }, + { + "name": "Profitability", + "description": "Product Profitability", + "id": "kpi-business-002", + "url": "/kpis/profitability", + "version": "V 1.2", + "status": "Declining", + "trend": "-6%", + "target": "45%" + }, + { + "name": "Cost to Serve", + "description": "Cost to Serve my product", + "id": "kpi-business-003", + "url": "/kpis/cost-to-serve", + "version": "V 1.2", + "status": "Declining", + "trend": "-2.5%", + "target": "$18.2M" + }, + { + "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": "Performance Report", + "description": "Detailed analysis of system performance metrics", + "id": "report-performance-001", + "url": "/reports/performance", + "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" + } + ] + } + ] +} diff --git a/__intelliarcs/meta.json b/__intelliarcs/meta.json new file mode 100644 index 0000000..315a703 --- /dev/null +++ b/__intelliarcs/meta.json @@ -0,0 +1,26 @@ +[ + { + "id": "experience-360", + "file": "experience-360.json", + "name": "Experience 360", + "description": "Revolutionize customer engagement with Experience360 a powerful AI-driven platform designed for both agents and customers.", + "status": "87% Complete", + "statusType": "progress" + }, + { + "id": "exceptions-360", + "file": "enrich-360-nasa.json", + "name": "Exceptions 360", + "description": "Smart, all-in-one platform for managing exceptions with ease. By integrating seamlessly with the external systems, it empowers teams to track, resolve, and streamline exception handlingβ€”all from one centralized hub.", + "status": "Running", + "statusType": "active" + }, + { + "id": "enrich-360", + "file": "enrich-360.json", + "name": "Enrich 360", + "description": "Empower product managers with proactive, actionable insights that drive revenue growth, boost profitability, and build lasting customer loyalty.", + "status": "Running", + "statusType": "active" + } +]