Timeline
2 – 8 weeks
Location
Wollongong, AU
Overview
I integrate large language models Claude, GPT-4o, Gemini, and Groq directly into your web applications and business systems. From AI chatbots and document Q&A systems to intelligent data processing pipelines, I build production-ready AI features with proper error handling, cost control, prompt caching, and monitoring. AI integration is not a side interest it is a core specialisation backed by real delivered projects.
AI integration is more than calling an API. The difference between a demo and a production feature is everything around the model: prompt design that produces consistent results, structured outputs your code can rely on, retries and fallbacks when a provider has a bad day, cost controls so a traffic spike does not become a billing surprise, and evaluation so you know quality is holding up. That production layer is what I build.
I work across the major providers Anthropic's Claude, OpenAI, and open models via OpenRouter and recommend the model based on your task, latency budget, and data requirements rather than hype. Where your use case needs your own knowledge to power answers, I build RAG pipelines with embeddings and vector search so the AI responds from your documents instead of guessing.
What's Included
- LLM API integration (Claude, GPT-4o, Gemini, or Groq)
- Prompt engineering and system prompt design
- RAG (Retrieval-Augmented Generation) pipeline with vector search
- Streaming responses for real-time UX
- Prompt caching to reduce API costs
- Token usage monitoring and cost dashboard
- Fallback handling and rate limit management
- Production deployment with environment security
Key Features
Multi-Model Support
Claude 4 (Anthropic), GPT-4o (OpenAI), Gemini (Google), and Groq I select the right model for your use case and budget, or build multi-model routing.
RAG Systems
Retrieval-Augmented Generation connects your AI to your own documents, knowledge bases, or databases so it answers questions with your data, not generic training data.
AI Chatbots & Assistants
Customer support bots, internal knowledge assistants, sales qualification agents built with proper conversation memory, guardrails, and escalation paths.
Streaming & Real-Time UX
Token-by-token streaming responses make AI features feel instant and responsive, dramatically improving user experience over batch responses.
Cost Optimisation
Prompt caching, model routing, and token budgeting keep API costs low. I build cost dashboards so you always know what you're spending.
Production Security
API keys isolated in server-side environment variables, input sanitisation, rate limiting, and PII handling AI features built safely for production.
How I Work
- 1
Use-case scoping
We identify where AI genuinely adds value in your product or operations and where a simpler, cheaper non-AI solution would honestly serve you better.
- 2
Model & architecture selection
Choosing the right provider, model size, and pattern (direct calls, RAG, agents) for your accuracy, latency, privacy, and cost requirements.
- 3
Prototype
A working proof-of-concept against your real data, so we validate quality and cost-per-request before committing to a full build.
- 4
Production hardening
Streaming, error handling, rate limiting, prompt versioning, cost monitoring, and guardrails the unglamorous work that makes AI features dependable.
- 5
Launch & iterate
Deployment with usage analytics, then iteration on prompts and retrieval quality based on how real users actually interact with the feature.
Ways to Work Together
Fixed-Scope Integration
A defined AI feature chatbot, document analysis, content pipeline, RAG search built into your product for an agreed quote.
Best for: Adding a specific AI capability to an existing product.
AI Retainer
Ongoing capacity for prompt tuning, model upgrades, new AI features, and keeping pace as providers ship new capabilities.
Best for: Products where AI is core and quality must keep improving.
AI Strategy Consulting
An audit of where AI fits your business, model and architecture recommendations, and a realistic build-vs-buy assessment.
Best for: Leaders deciding where to invest in AI before committing budget.
Real Project Example
AI content pipeline with OpenRouter structured, reviewed, automated
For my LinkedIn automation system, I integrated LLMs via OpenRouter to generate post copy with structured JSON outputs, paired with AI image generation, and wrapped it in an n8n pipeline with human review via Google Sheets before publishing. It is a working example of AI integration done properly: structured outputs, human-in-the-loop, and full automation around the model.
- Structured outputs guarantee parseable, consistently formatted AI responses
- Human approval step keeps quality control without manual writing
- AI image generation matched to each post automatically
- Full architecture documented publicly on the blog
Serving Wollongong & All of Australia
From Wollongong, I work with businesses across NSW and Australia-wide. For Australian companies with data-residency concerns, I design AI integrations where your documents and customer data stay in Australian cloud regions, with only the minimum necessary context sent to model providers and I will tell you plainly what each provider does with your data.
Frequently Asked Questions
Further Reading
Related Services
Ready to get started?
Tell me about your project and I will get back to you within 24 hours with a plan and quote.