I build systems that don’t fall apart in production.

An AI systems architect designs and builds the infrastructure that makes AI usable in production: RAG pipelines that retrieve accurate information at scale, LLM integrations connected to real business systems (CRMs, CMSs, payment gateways), and automation workflows that replace manual processes reliably. The role bridges software architecture and AI engineering — not building demos, but systems that handle real traffic, real edge cases, and real data.

Most AI projects fail in production because they were built for a demo environment: clean data, no concurrency, no error states, no cost constraints. Production systems deal with malformed inputs, API rate limits, ambiguous queries, and unexpected load. Bridging that gap requires treating AI components as infrastructure — with the same logging, fallback handling, and architectural discipline as any production service.

A RAG (Retrieval-Augmented Generation) pipeline retrieves relevant documents from a knowledge base at query time, then generates an answer grounded in that retrieved content. You need one when accuracy matters: regulated industries, support systems with proprietary product knowledge, or any context where a hallucinated answer has real consequences. A stuffed system prompt breaks down at scale; RAG keeps the knowledge base separate, auditable, and updatable.

Both. As an AI engineering consultant and fractional technical lead, I embed with teams that need senior architecture capacity without a full-time hire — owning technical decisions, unblocking developers, translating tradeoffs for stakeholders. I also work directly with founders and operators on specific scoped projects: RAG systems, automation pipelines, WordPress AI integrations.

A fractional technical lead acts as your senior engineering decision-maker for a defined number of hours per week, without the cost or commitment of a full-time hire. Owning architecture decisions, reviewing critical code paths, unblocking developers, and translating technical tradeoffs into language stakeholders can act on. Every engagement has a defined scope and hours — not an open-ended arrangement.

Yes. Typical integrations include AI-powered customer support chatbots connected to your product catalog and order history, content generation pipelines that draft posts for human review, and n8n automation workflows that replace manual weekly processes. The work connects to your existing infrastructure — no platform migration required.

Architecture decision records explaining why key choices were made, integration guides for connecting other systems to what was built, operational runbooks for common failure scenarios, and onboarding documentation sufficient for a new developer to understand and maintain the system without me present. Documentation is a first-class deliverable — not an afterthought.

I start by separating process symptoms from process causes. Teams that miss deliveries are usually dealing with one of three things: unclear scope that expands mid-sprint, architectural debt that makes every change take twice as long, or a missing decision-maker who creates blockers that sit unresolved. The fix for each is different. The engagement starts with access to the work — tickets, code, commit history — not just conversations with people.