The Modern AI Application Stack
A Technical Guide for Enterprise Leaders
A comprehensive guide to building production-ready AI applications. Learn the architecture patterns, infrastructure requirements, and best practices used by leading enterprises to move from AI pilots to production-grade solutions.
What You'll Learn
- Reference architecture for enterprise AI applications
- Infrastructure patterns that scale from POC to production
- Security, governance, and compliance considerations
- Real-world lessons from Fortune 500 deployments
- Build vs. buy decision frameworks
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of organizations are getting zero return from GenAI investment
Source: MIT NANDA, State of AI in Business 2025
It's not the model. It's the stack.
Most AI pilots fail not because of the technology, but because organizations underestimate the infrastructure required to run AI at scale.
- → Model gateways & routing
- → Context & retrieval systems
- → Tool orchestration & durability
- → Evaluation & improvement loops
- → Safety & governance layers
What's Inside This Whitepaper
A comprehensive blueprint covering every layer of production AI architecture
Each section includes architecture patterns, technology recommendations, and lessons from real implementations.
Who Should Read This
Built for technical leaders navigating the AI infrastructure landscape
CTOs & VPs of Engineering
Evaluating AI infrastructure decisions and build vs. buy trade-offs
Data Science Leaders
Moving from experimentation to production-grade AI systems
Enterprise Architects
Designing scalable AI systems that integrate with existing infrastructure
Technical Decision Makers
Navigating vendor selection and the rapidly evolving AI landscape
Key Topics Covered
Foundation & Infrastructure
- • Task-focused UIs vs. chat interfaces
- • Human-in-the-loop by design
- • Database portfolio strategy (OLTP, OLAP, Vector, Graph)
- • Business context and semantic modeling
Core Services & Orchestration
- • Memory architectures per use case
- • MCP servers and tool integration patterns
- • Durable execution with Temporal/DBOS
- • Model gateway and semantic caching
Intelligence & Safety
- • Layered safety systems and guardrails
- • Prompts as versioned, testable artifacts
- • Evaluation frameworks and golden datasets
- • Online vs. offline monitoring strategies
Governance & Improvement
- • A/B testing prompts and model swapping
- • Fine-tuning readiness and data bootstrapping
- • AI Center of Excellence operating model
- • Security, compliance, and audit patterns
Why Download This Whitepaper?
Actionable Insights
No fluff—practical frameworks you can apply immediately.
Real-World Examples
Based on actual enterprise AI implementations.
Expert Perspective
Written by practitioners, not theorists.
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Our team has helped dozens of organizations move from AI strategy to production-grade solutions.
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