Production AI
- Multi-provider routing, budget controls, and quality guardrails
- Evaluation loops, prompt lifecycle, and deployment discipline
- Systems designed for reliability, not just first-run demos
Raymond Clanan · principal engineer
Production AI, search, and backend systems built with an operator’s discipline.
I work with teams that need real systems, not demo theater: retrieval, routing, observability, performance, and delivery that holds up after launch.
Choose your path
Start with the asset that matches your intent.
01
I’m evaluating fit
Use the short path if you need to understand how I work, what I ship first, and where I’m strongest.
Open start here 02I need proof
Go straight to outcomes, architecture decisions, and the constraints behind enterprise and SaaS engagements.
Read case studies 03I want technical depth
Dig into writing on AI delivery, routing, observability, RAG, performance, and the tradeoffs behind production work.
Browse writingWhat I actually do
Architecture, implementation, and operating discipline in one seat.
Search and RAG
- Retrieval pipelines with pgvector, citations, re-ranking, and freshness
- Chunking and ingestion that survive changes in source content
- Latency-aware designs that make retrieval usable in real workflows
Backend systems
- Low-latency services in Rust and .NET
- Observability from day one: logs, traces, metrics, cost signals
- Developer experience that supports fast iteration without chaos
Selected work
Two examples of technical decisions turning into measurable business outcomes.
Volvo · Infosys
40% fasterCloud-agnostic CQRS framework for a distributed team.
Designed an event-sourcing and CQRS foundation that improved delivery speed across eight squads while meeting audit and compliance expectations.
.NET CoreCQRSEvent sourcing
Read the engagementGrowth-stage SaaS
60% / 30%LLM routing layer with lower latency and tighter cost control.
Built intelligent provider routing with telemetry and budget controls, reducing latency by 60% and model spend by 30%.
RustLLM opsObservability
See the playbookWriting
Notes for founders and engineering teams shipping real systems.
Build-in-Public: My Quarterly Roadmap
A clear quarterly roadmap for offer positioning, delivery systems, and product priorities across my portfolio.
What a Good Automation Project Looks Like Before We Start
The pre-build checklist I use to define scope, readiness, ownership, and rollout before automating a workflow.
The Production AI Tooling Stack I Use (and Why)
How I choose the tools behind production AI systems based on observability, reliability, operational fit, and cost.
Next step