Insights
Product, design, AI, and engineering perspectives from our team.
AI Localisation Architecture for APAC Market Expansion
Expanding into New Zealand and APAC takes more than a translation layer. This guide walks through the architectural decisions — retrieval, data structure, model selection, and locale-specific UX — that determine whether a multi-language AI rollout scales cleanly or needs a rebuild.
RAG Implementation Consulting: How It Works and When to Use It
Retrieval-Augmented Generation (RAG) is an LLM architecture pattern that grounds model output in retrieved documents at inference time — making it one of the most practical approaches for enterprise knowledge retrieval. This article explains how RAG works, when it is preferable to fine-tuning, and what a production-grade implementation actually involves, including Australian data sovereignty considerations.
Custom AI Agent Development: Architecture and Use Cases
AI agents are autonomous software systems that plan, use tools, and execute multi-step tasks — a significant step beyond standard LLM calls. This guide covers the core architectural patterns, industry use cases, data prerequisites, and what a responsible commissioning process looks like for teams considering custom AI agent development.
Context Engineering for LLM Apps: Beyond Prompt Templates
Prompt templates are where LLM applications start. Context engineering is what makes them work reliably in production. This article covers the four core levers — retrieval, compression, memory, and ordering — and how to build a context pipeline that produces consistent, cost-efficient model behaviour at scale.
Structured Outputs and Function Calling: Making LLMs Reliable
Structured outputs and function calling are the mechanisms that make LLMs viable in production workflows — but reliable implementation requires deliberate schema design, validation layers, and observability from the start. This guide covers how to use both patterns effectively, when to choose each, and the failure modes that catch teams off-guard at scale.
LangChain vs LlamaIndex vs Vercel AI SDK: Choosing an AI Framework
LangChain, LlamaIndex, and the Vercel AI SDK each solve different problems — and picking the wrong one creates real maintenance overhead for your engineering team. This guide breaks down the strengths, trade-offs, and ideal use cases for each framework so you can make a grounded architecture decision.