Insights
Product, design, AI, and engineering perspectives from our team.
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.
Build vs Buy vs Partner: AI Adoption Paths for Mid-Market
Mid-market companies face a critical choice when adopting AI: build custom solutions in-house, buy off-the-shelf SaaS tools, or partner with specialists. Understanding the trade-offs in cost, speed, control, and risk helps you make the right decision for your specific context.
How to Write an AI Project Brief That Gets Accurate Quotes
A well-written AI project brief eliminates mismatched vendors early and attracts qualified consultancies. Focus on business problems rather than technical prescriptions to get accurate, comparable quotes.
AI Consulting Pricing: Fixed Price vs Time & Materials vs Retainer
Understanding AI consulting pricing models helps you choose between fixed price certainty, time & materials flexibility, or retainer continuity. Australian market ranges from $30,000 proof-of-concepts to $800,000+ platform builds.
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.
Building Production RAG Systems: Beyond the Demo to Reliable Scale
Most RAG demos work beautifully with perfect documents and cherry-picked queries. Production RAG systems face messy reality — document diversity, edge cases, and 99%+ accuracy expectations that require systematic engineering across chunking, embedding, retrieval, and monitoring.