Insights
Product, design, AI, and engineering perspectives from our team.
From AI Pilot to Production: Scaling Your Proof of Concept
Most AI pilots never reach production deployment due to the gap between proof of concept and operational readiness. Learn how to bridge this gap with proper infrastructure, monitoring, and team planning.
How to Evaluate RAG System Quality: Metrics That Actually Matter
Comprehensive guide to evaluating RAG system quality in production. Learn essential metrics for retrieval precision, answer faithfulness, and operational performance to ensure reliable AI-powered applications.
Scaling AI Pilots to Production: The Technical Reality in Australia
Moving from AI pilot to production requires more than scaling up—it demands different architecture, skills, and processes. Most pilots skip the infrastructure and operational complexity needed for real-world deployment.
Vector Database Architecture for RAG Applications in Australia
Vector databases form the backbone of RAG applications, but selecting the right architectural approach requires understanding trade-offs between managed services, open source solutions, and PostgreSQL extensions. Australian organisations must also consider data sovereignty and compliance requirements when making these decisions.
Building Production-Ready AI Systems: Our Development Approach
Learn how Horizon Labs builds production-ready AI systems with comprehensive MLOps, vendor independence, and business-centric strategies. Our approach ensures AI solutions that work reliably in the real world.
AI Agents That Work: Architecture Patterns for Multi-Agent Systems
Multi-agent AI systems are becoming production reality for Australian enterprises, but most implementations fail due to poor architecture choices. Learn the orchestration patterns, communication protocols, and error handling strategies that separate proof-of-concept demos from production-ready systems.
On-Device AI for Mobile Apps: When Edge Beats Cloud
On-device AI processes machine learning directly on mobile devices, delivering sub-100ms response times and offline functionality. This guide covers Core ML, TensorFlow Lite, model optimisation, and real-world applications for Australian mobile development.
RAG vs Fine-Tuning: When to Use Each (And When You Don't Need Either)
RAG and fine-tuning serve different purposes in LLM deployment, with distinct cost, performance, and maintenance profiles. Most organisations jump to complex solutions when simple prompt engineering would suffice.
MLOps Explained: How Production AI Stays Reliable After Launch
MLOps ensures AI models remain accurate and reliable in production through continuous monitoring, automated retraining, and governance frameworks. Learn how to detect model drift, implement monitoring pipelines, and build automated retraining systems that keep production AI performing at peak effectiveness.