Insights
Product, design, AI, and engineering perspectives from our team.
Multi-Agent Orchestration: Semantic Kernel vs AutoGen vs LangGraph
Semantic Kernel, AutoGen, and LangGraph represent three genuinely different bets on how multi-agent systems should be structured. This decision guide covers orchestration models, state management, production-readiness, and how to match the right framework to your problem — before you commit to an architecture you will live with.
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.
Multi-Model Routing: Cut LLM Costs Without Sacrificing Quality
Multi-model routing sends each AI request to the cheapest model capable of handling it well, rather than routing everything through a frontier model. Combined with semantic caching and fallback chains, it is one of the most effective ways to control LLM costs in production without degrading quality. This post covers the core patterns and what a practical production architecture looks like.
Open-Source vs Proprietary LLMs: When to Self-Host
Self-hosting open-weight models like Llama or Mistral gives Australian teams data residency, cost control at scale, and deployment flexibility — but it comes with real infrastructure and operational tradeoffs. This post works through when each approach makes sense, with a practical framework for mid-market teams deciding between open-weight self-hosting and proprietary APIs like GPT-4 or Claude.
AI Product Strategy: A Consulting Framework for Australian Businesses
AI product strategy is the structured process of deciding where AI creates real value in your product, what it takes to build it, and how you measure success. This article walks through the four-stage consulting framework Horizon Labs uses — from readiness assessment through implementation planning — to help Australian businesses move from intent to execution.
Application Modernisation Cost Guide for Australia
Application modernisation costs in Australia vary significantly depending on your architecture, chosen approach, and cloud provider. This guide breaks down realistic cost ranges for common modernisation paths — from rehosting to full re-architecture — so you can plan and budget with confidence.
AI in Australian Manufacturing: 5 Use Cases in Production
Five AI use cases Australian manufacturers are running in production today — predictive maintenance, computer vision quality inspection, document AI for compliance, demand forecasting, and procurement automation. A practitioner's view of what the architecture looks like, what you need in place, and where each use case gets hard.
Digital Engineering Consultancy Australia: Beyond AI — The Full Stack
A digital engineering consultancy works across the full technology stack — applications, data infrastructure, cloud platforms, DevOps, and AI — delivered as a coherent system. In the Australian mid-market, that integration is what separates technology investments that compound from those that stall. This article explains what the full stack looks like, why fragmented delivery fails, and what an end-to-end Australian consultancy actually does.
Application Modernisation: The Strangler-Fig Pattern in Practice
Rewriting a legacy monolith from scratch is high-risk and rarely necessary. The strangler-fig pattern lets Australian engineering teams modernise .NET and Java applications incrementally — shipping value continuously while reducing risk. Here is how it works in practice.