Multi-provider LLM routing system
Intelligent routing across OpenAI, Azure OpenAI, and Anthropic with cost optimization and budget enforcement.
Raymond Clanan
Production AI Systems
Comprehensive guide to building, deploying, and scaling production-grade AI and LLM systems with real-world expertise
Building AI systems that actually work in production requires more than just fine-tuning models. It's about architecture, cost control, observability, and reliability at scale.
My expertise
I specialize in production-ready AI systems that go beyond the POC stage. Whether it's multi-provider LLM routing, RAG pipelines, cost optimization, or evaluation frameworks — I've built systems that handle real-world scale and complexity.
What I focus on
Architecture
Cloud-agnostic patterns, CQRS for AI workloads, event-driven architectures, and multi-model orchestration.
Cost optimization
Intelligent routing, caching strategies, prompt optimization, and budget enforcement to manage LLM spending and evaluate cost tradeoffs.
RAG systems
Hybrid search, chunking strategies, vector databases, reranking, and evaluation frameworks for production RAG.
Observability
Custom metrics, trace analysis, quality evaluation, cost tracking, and real-time monitoring for LLM systems.
Performance
Latency optimization, streaming responses, async processing, batch inference, and edge deployment.
Safety and security
Input validation, prompt injection defense, PII redaction, guardrails, and content moderation.
Case studies
Real-world AI systems I've designed and built:
Enterprise RAG on Azure
Production RAG system with pgvector, cross-encoder re-ranking, content-aware chunking, and an offline evaluation pipeline.
Related writing
- RAG on Azure with pgvector: Practical Patterns for Production — a practical architecture for production RAG on Azure with .NET, Postgres, and pgvector.
- Observability for LLM apps: Logs, Traces, and Feedback — the signals that matter when LLM features fail silently, drift in quality, or get too expensive.
- The Smallest Eval Loop That Keeps an AI Feature Honest — real examples, a place to look at failures, and a habit you actually keep.
Free resource
The Production AI Systems Checklist covers eight categories, from architecture to deployment, for shipping AI systems to production.
Common questions
How much does it cost to work with you on an AI project?
Architecture and readiness reviews run $5K–$10K (1–2 weeks), focused pilots $15K–$50K (2–6 weeks), and production builds $50K–$300K+ (3–12 months), depending on scope and integrations. Ongoing advisory is $5K–$15K/month. View full pricing.
Do you work with specific LLM providers?
I'm provider-agnostic and have deep experience with OpenAI, Anthropic, Google (Gemini), Cohere, Azure OpenAI, AWS Bedrock, and open-source models (Llama, Mixtral, etc.). My approach focuses on multi-provider routing for reliability and cost optimization.
Can you help with existing AI systems that aren't performing well?
Absolutely. I offer AI System Audits where I review your architecture, evaluate costs, identify performance bottlenecks, and provide a detailed report with actionable recommendations. This is often a good starting point before larger optimization work.
What's your approach to RAG systems?
I focus on hybrid search (vector + keyword), intelligent chunking strategies, reranking, and comprehensive evaluation. I've built RAG systems handling millions of documents with 85%+ retrieval accuracy and sub-2-second response times.
Related topics: Performance Optimization · Observability · More case studies