AI Solutions Architect Microsoft Azure Industry Validated
Enterprise AI Architect | AICapabilityBuilder.com
π Hands-on AI implementation with rapid prototyping expertise π― Industry-Validated Framework: Adapted from Gartner, McKinsey, MIT CISR, IBM, Microsoft πΌ Three-Layer Architecture + Governance: Production-ready enterprise AI solutions
Framework Source: Adapted from Gartner AI Maturity Model, McKinsey Strategic AI Framework, MIT CISR Enterprise AI Maturity Model, IBM AI Operating Model, Microsoft AI Transformation Journey
Enterpise AI - 3 Layered Approach_cropped
Intelligent interfaces that users actually want to use
stealth-sales-coach AI-Powered Sales Coaching - Real-time conversation analysis and coaching suggestions π§ Tech: Azure AI, RAG, Semantic Kernel | π Impact: 25% higher win rate
harringey-voicechatbot-AZURE Voice-Enabled Chatbot - Multi-language support with Azure Cognitive Services π§ Tech: Azure Speech, OpenAI | π Impact: 40% faster customer resolution
Layer 1 Capabilities:
- β Microsoft 365 Copilot custom plugins
- β Conversational AI with advanced RAG
- β Voice-enabled multi-language interfaces
- β Real-time assistance and coaching
Transform organizational data into continuous learning intelligence
strategic-forecasting-ai Strategic Forecasting System - Executive decision support with scenario planning π§ Tech: Azure AI Foundry, AutoML | π Impact: 300% ROI, strategic insights
ML_Fraud Fraud Detection with Continuous Learning - Pattern recognition that improves over time π§ Tech: H2O.ai, Azure ML | π Impact: 87% accuracy (60% β 87% over 12 months)
inventory-intelligence-h2o Inventory Optimization - Demand forecasting with automated learning π§ Tech: H2O.ai, Azure Synapse | π Impact: 25% reduction in stockouts
skmultiagents Multi-Agent Orchestration - Semantic Kernel-based agent collaboration π§ Tech: Semantic Kernel, Azure OpenAI | π Impact: Complex workflow automation
Layer 2 Capabilities:
- β Memory + Learning: Fraud patterns, demand forecasting, continuous improvement
- β Compute: Real-time analytics, AutoML, predictive modeling
- β Configuration/Logic: Business rules, guardrails, compliance checks
Reliable, cost-optimized delivery infrastructure
genaiops-azureaisdk-template GenAIOps Template - Production-ready Azure AI infrastructure with MLOps π§ Tech: Azure AI SDK, Terraform, Kubernetes | π Impact: 30-50% cost reduction
MLOpsPython MLOps Best Practices - CI/CD pipelines for ML model deployment π§ Tech: Azure DevOps, GitHub Actions | π Impact: 99.9% uptime
Layer 3 Capabilities:
- β Orchestration: Kubernetes, GPU scheduling, workload optimization
- β Observability: Prometheus, Grafana, cost tracking
- β Security: Azure Key Vault, RBAC, compliance automation
- β Cost Optimization: Auto-scaling, spot instances, rightsizing
Responsible AI with built-in compliance
Coming soon: Dedicated governance repositories showcasing:
- β Data governance and privacy (GDPR, HIPAA)
- β Model governance and bias monitoring
- β Operational governance and audit trails
- β Ethical AI and risk management
three-layer-ai-framework β Three-Layer AI Framework - Complete production implementation with working code, case studies, deployment templates π§ Framework: Gartner + McKinsey + MIT CISR adapted | π Proven: 7-12x ROI over 24 months
enterprise-ai-analytics-platform β Enterprise AI Analytics Platform - AutoML, Natural Language Queries, Real-time Dashboards π§ Complete Stack: All 3 layers + governance | π Production: Azure-integrated, scalable
Microsoft Copilot Semantic Kernel Azure AI
| Architecture Layer | Implementation | Business Impact |
|---|---|---|
| π¨ Layer 1: UX | Copilot plugins + Voice chatbots | 85% adoption, 40% faster resolution |
| π§ Layer 2: Intelligence | Fraud detection + Forecasting | 87% accuracy, 300% ROI |
| βοΈ Layer 3: Infrastructure | GenAIOps + MLOps | 30-50% cost reduction, 99.9% uptime |
Azure Solutions Architect AI Engineer Associate TOGAF PRINCE2
Framework adapted from industry leaders: Gartner AI Maturity Model | McKinsey Strategic AI Framework | MIT CISR Enterprise AI Maturity | IBM AI Operating Model | Microsoft AI Transformation Journey
Book Architecture Consultation LinkedIn Connect Email Discussion
Proven methodology: Layer 3 (Infrastructure) β Layer 2 (Intelligence) β Layer 1 (UX) + Governance throughout β Measurable Business Impact
Profile Views GitHub followers
"Three-layer AI architecture: from foundation to intelligence to user experience, with governance throughout"