AI Agents & Automation for Australian Business — Nathan McBride
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Agents, RAG, automation · LLMs in prod since 2023

AI development,
past the demo.

AI agents, automation and integration for Australian businesses and teams worldwide — built by a developer, not a marketing agency. Strategy, production pilots, and AI wired into the commerce stacks your business already runs on.

Serving Sydney, Melbourne, Brisbane & clients worldwide — remote-first

Commerce brands I’ve shipped forRun Auto PartsBamfordDaylesfordBrandAlleyKathmanduiBuys
Capabilities

What I build.

A deliberately narrow list. These are the pieces I ship week in, week out — and the pieces I charge for with confidence.

01 / 06

AI agents & automation

Multi-step agents that triage inboxes, classify tickets, process orders, reconcile supplier data, and run outreach. Tool calling, retries, evaluation — not a chatbot with extra steps.

  • Tool calling
  • Multi-step
  • Evaluation
  • Horizon queues
02 / 06

RAG & knowledge assistants

Internal Q&A over your documents, wikis, tickets and CRM. Customer-facing assistants grounded in your content. Citations you can trust and an evaluation harness that catches drift.

  • RAG
  • Embeddings
  • pgvector
  • Citations
03 / 06

Ecommerce AI

Semantic product search, natural-language filtering, AI merchandising and personalised recommendations — wired into the Shopify or Magento stack you already run on.

  • Semantic search
  • Merchandising
  • Recs
  • Typesense
04 / 06

Content pipelines

Product descriptions, editorial, localisation and SEO at scale. Guardrails, human review gates, and brand-voice evaluation — content ops you can actually audit.

  • PDP copy
  • Localisation
  • SEO meta
  • Brand voice
05 / 06

MCP servers & integrations

Expose your internal systems to Claude Code, Cursor and Claude Desktop through Model Context Protocol. Secure tool surfaces for your team, your partners, or your customers.

  • MCP servers
  • Tool surfaces
  • Scoped auth
  • Observability
06 / 06

AI strategy & audits

The consulting tier. Opportunity mapping, build-vs-buy analysis, vendor and model selection, ROI modelling, and a roadmap you can actually execute.

  • Opportunity map
  • ROI model
  • Vendor selection
  • Roadmap
How I engage

Three ways in.

Pick one, or sequence them
In depth

AI, in detail.

01 · AI automation built by an engineer, not an agency

AI automation built by an engineer, not an agency

The AI agency market is full of marketing-led operations reselling no-code workflows. I am a software engineer who has shipped commerce platforms for over a decade and has been putting LLMs into production since 2023 — and against machine-learning APIs since well before that. When I build an agent, it has tool calling, retries, evaluation and a kill switch, because it is software, not a Zapier chain with a prompt taped on.

02 · Custom AI agent development

Custom AI agent development

The useful gap between "we bought Copilot seats" and "we hired a data science team" is custom agents: AI wired into your actual systems that does jobs rather than advising on them. Support triage grounded in your docs, invoice and document processing, product data enrichment, content pipelines with human review gates. I scope each one against an ROI model before building, and ship to production with telemetry — not to a demo environment.

03 · AI for ecommerce

AI for ecommerce

This is the crossover almost no AI agency in Australia can offer: deep platform experience on Magento and Shopify plus production LLM work. Semantic product search, natural-language filtering, AI merchandising, automated product copy at catalogue scale — integrated into the storefront you already run, by someone who knows both sides of that integration.

04 · Where I work

Where I work

I work with businesses across Australia — Sydney, Melbourne, Brisbane and beyond — and internationally, remote-first with Australian business-hours overlap. Typical briefs:

  • 01AI agents: support triage, document processing, ops automation
  • 02RAG knowledge assistants over your own content
  • 03Ecommerce AI: semantic search, merchandising, product copy
  • 04MCP servers exposing your systems to AI tooling
  • 05AI strategy: opportunity mapping, build-vs-buy, ROI models
  • 06Data-residency-aware deployments, including self-hosted models
Worth knowing

Data privacy and Australian data residency

You do not have to send your data offshore to use AI. Zero-retention enterprise API tiers, Azure-hosted models inside your own tenant, or fully self-hosted open-source models — I run all three in production and match the posture to your compliance requirements.

Ecosystem

The stack I live in.

The tools I reach for without thinking. Your stack may differ — I meet it rather than fight it.

20 tools
  • 01laravel/ai
  • 02Anthropic Claude
  • 03OpenAI
  • 04Google Gemini
  • 05Llama
  • 06MCP
  • 07Vercel AI SDK
  • 08Ollama
  • 09pgvector
  • 10Pinecone
  • 11Typesense
  • 12Voyage embeddings
  • 13Vision models
  • 14Whisper
  • 15ElevenLabs
  • 16LangFuse
  • 17Horizon
  • 18Redis
  • 19PostgreSQL
  • 20Python
FAQ

Questions I get,
answered straight.

Missing one? Ask in the form — I reply within a working day.

01Should we build custom or buy ChatGPT Enterprise / Copilot?
Buy for the generic work — drafting, summarising, research — every business gets value from a seat licence. Build custom when AI touches your product, your customers, or a workflow specific to how your business runs. The moment you need tool calling into your own systems, grounding in your own data, or an agent that actually does the job rather than advises on it, off-the-shelf runs out of runway. I will tell you honestly which side of that line your brief sits on.
02What is the difference between a chatbot, an agent, and automation?
A chatbot talks. An agent decides and acts — multi-step, with tools, with a goal. Automation runs an agent (or a simpler rule) on a schedule or trigger without a human in the loop. Most buyers arrive asking for a chatbot and actually need an agent wired into automation. The shape of the work changes dramatically based on which one you need, which is why I scope it properly before I quote.
03How do you measure ROI on AI work?
Before I build, I write the ROI model — hours saved, conversions lifted, ticket deflection, cost per task — and I agree what counts as the baseline with you. After I ship, I wire telemetry into the same metrics. If a pilot cannot defend its cost in month three, I say so and recommend killing it rather than expanding it. AI budgets get burned by projects nobody wants to declare dead; I avoid that by measuring from day one.
04Which model — Claude, OpenAI, or open source?
Claude for reasoning, long contexts, and agentic work. OpenAI for the broadest tool ecosystem and lowest-friction start. Gemini for cheap multimodal. Open source (Llama, Qwen, local deploys) when data residency, cost at scale or latency rules out everything else. I pick per-workload rather than per-vendor, and I build on laravel/ai so swapping is a config change, not a rewrite.
05Can we use AI without sending our data to OpenAI?
Yes. Options in rough order of adoption: Anthropic and OpenAI enterprise tiers with zero-retention contracts; Azure-hosted OpenAI inside your tenant; self-hosted open-source models (Llama, Qwen) through Ollama or vLLM. I run all three in production for different clients and will match the posture to your compliance requirements — GDPR, PCI, healthcare — rather than defaulting to the cheapest API.
06How do you handle hallucinations and failures in production?
Three layers. Grounding (RAG with citations, structured output, tool calling over chat) removes the most common failure mode. Evaluation (golden datasets, LLM-as-judge on a held-out set, prompt regression tests in CI) catches drift before users do. Observability (LangFuse or custom telemetry, cost and latency dashboards, prompt/response logging) catches the rest. Nothing I ship is "pray it works" — every agent has a kill switch and a rollback.
07How much does custom AI development cost?
A two-week strategy engagement is fixed-price and gives you an ROI-ranked roadmap before any build spend. Pilots are quoted fixed or capped — typically a four-to-eight week engagement shipping one agent or automation to production. I also model the ongoing inference cost per task before you commit, so there is no surprise API bill in month two.
08Do you build AI solutions for Australian businesses?
Yes — I work with Australian businesses remote-first with full local business-hours overlap, and I take Australian data-residency requirements seriously: zero-retention API tiers, in-tenant Azure deployments, or self-hosted models when the compliance posture demands it.
Start a project

Got an AI
workflow to ship?

Two-week strategy audit, a four-week pilot shipped to production, or a full platform build. Tell me what you're trying to automate and I'll tell you the shortest path to value.

[email protected] — I reply within one working day, personally

2023
LLMs in production
4
Model providers wired
MCP
Server & client work
1d
Response time