Enterprise AI Solutions Architecture: The Practitioner’s Handbook for Designing, Delivering, and Scaling Production AI Systems

Most AI projects fail in production. Not because the models are wrong — because the architecture around them wasn't built for scale. Enterprise AI Solutions Architecture is a practitioner's reference for AI Solutions Architects from junior through Principal level — the professionals who sit between engineering teams and business stakeholders and are responsible for making AI systems that actually work at enterprise scale. This is not a survey of tools. It is not a certification guide. It is a judgment-first architecture handbook built on twenty years of production AI engagements across banking, healthcare, industrial operations, retail, telecommunications, and the public sector. The right technical answer changes every 18 months. The reasoning framework does not. That reasoning framework is what this book transfers. What makes this book different: Every recommendation is tied to a business constraint, a failure mode, or a real architectural tradeoff — not vendor preference Platform-neutral by design: AWS, Azure, and GCP are treated as equivalent platforms — the right choice follows the client's existing commitment, not a vendor's marketing budget Layer-signaled for three experience levels: Foundation for junior SAs, Architecture depth for senior SAs, and Principal-level judgment throughout Six full industry playbooks — each with a complete end-to-end reference case written at the level of a real client engagement The 27 chapters cover: RAG deep architecture — chunking strategies, hybrid retrieval, Graph RAG, multi-modal RAG, and production evaluation frameworks Agentic AI — planning architectures, multi-agent coordination, tool use design, human-in-the-loop patterns, and MCP/A2A interoperability Inference architecture — latency optimization, GPU selection, auto-scaling, multi-model serving, and token economics at scale MLOps and LLMOps — experiment tracking, model registries, drift detection, LLM observability, and managing 100+ models across the enterprise AI security and governance — prompt injection defense, the LLM Gateway pattern, zero trust architecture, EU AI Act, SR 11-7, HIPAA, and FedRAMP Industry playbooks for Financial Services, Healthcare and Life Sciences, Industrial and Energy, Retail, Telecommunications, and Public Sector and Sovereign AI The Chapter 27 Toolkit includes 14 immediately deployable templates:Use Case Prioritization Grid, Vendor Evaluation Scorecard, 40-Point Production Readiness Checklist, Build/Buy/Fine-Tune Decision Tree, Organizational Readiness Assessment, Executive Briefing One-Pager, Governance RACI, Model Risk Assessment aligned to SR 11-7, Responsible AI Pre-Launch Review, and a 90-Day First Engagement Playbook. About the author: Sasikanth Padigala is a Principal Solutions Architect with twenty years of experience in cloud and AI/ML architecture. He holds certifications across the full enterprise AI stack — NVIDIA Agentic AI Architect, Databricks Generative AI, Google Cloud, Microsoft Azure, AWS, and Oracle Cloud — and has delivered production AI systems across the most demanding regulated industries in the world. If you design, deliver, or govern AI systems at enterprise scale — this is the reference you have been looking for.