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AI Integration Consultant for Australian Teams

AI integration consultant for Australian teams

Anthropic SDK, OpenAI SDK, real production workflows. Not demos. Working APAC hours. EU-incorporated, AUD or EUR invoicing.

Why most AI projects fail in production

The demo works. The chatbot answers correctly. Then it hits real users, real edge cases, real cost budgets. Suddenly the rate limits matter, the prompt caching matters, the tool call schema matters, the fallback paths matter. "It worked locally" is where 80% of AI projects die.

I build AI features that survive production. Anthropic and OpenAI SDKs, prompt caching, structured outputs, tool use, evals, cost tracking. Not theory: real client projects shipping AI in workflows users actually depend on.

What I do

  • AI feature integration. Adding Claude or GPT capabilities to existing apps, sanely.
  • Prompt engineering with evals. Not "vibes", measurable test suites.
  • Workflow automation. Internal tools that save your team hours per week.
  • RAG and semantic search. Vector databases, embeddings, retrieval that actually retrieves the right thing.
  • Cost optimization. Prompt caching, model selection, batching. Usually cuts AI costs by 40 to 70 percent.
  • Production observability. Logging, monitoring, evals running on real traffic.

Proof, not promises

  • Flowrence (healthcare): production architecture relevant to AI features handling sensitive data.
  • Gems XYZ (Web3 social): real-time systems with external API integrations.

I also run a Skool community ("The Agentic Architect Lab") for technical founders shipping AI features. The patterns in my consulting work come from real production, not LinkedIn theory.

How engagements work

  • Discovery first: a paid 1-week scoping engagement before any build. We map the actual workflow, identify the AI-suitable steps, agree on success metrics.
  • Then build sprints, two to eight weeks.
  • Invoice in AUD, EUR or USD.
  • Async-first.

FAQ

Should we build with Anthropic, OpenAI, or something else? Both, often. Claude for reasoning and longer context, GPT for some structured tasks, open-source models for high-volume cheap inference. I'll recommend a stack based on your actual use case, not vendor loyalty.

How do we know the AI is actually working? Evals. Test cases run on every prompt change. Without evals, you're guessing.

What about hallucinations and accuracy? Designed for, not hoped against. Constrained outputs, tool use with verification steps, human-in-the-loop where the stakes warrant it.

Contact

If you'd like to discuss a project or just figure out whether we're a fit, get in touch via the contact page. No sales pitch.