Learn to Build Private AI That Works
In-depth guides, technical tutorials, and strategic frameworks from our AI engineering team. Written for technical leaders evaluating or building custom LLM solutions.
Book a Strategy SessionLLM Architecture
Deep dives into transformer architectures, model selection, and how to design AI systems that scale with your business data.
Choosing the Right Foundation Model for Your Enterprise
A practical framework for evaluating open-source and commercial LLMs against your specific accuracy, latency, and compliance requirements.
Transformer Architecture Explained for Business Leaders
A non-technical overview of how large language models work, what they can and cannot do, and how to set realistic expectations for your AI project.
On-Premises vs Cloud LLM Deployment: A Decision Framework
Trade-offs between deploying your custom model on-premises, in a private cloud, or on managed infrastructure, with cost modelling for each.
Data Privacy & Compliance
Navigate Australian data regulations, build compliant AI systems, and protect your organisation's most sensitive information.
Australian Data Residency Requirements for AI Systems
A comprehensive guide to the Privacy Act 1988, APRA CPS 234, and how they apply to organisations training AI models on sensitive data.
SOC 2 Compliance for AI Infrastructure
What SOC 2 Type II certification means for your AI deployment, and how to ensure your model training pipeline meets the standard.
Data Sovereignty in the Age of Foundation Models
Why sending your proprietary data to overseas API providers creates risk, and how private models eliminate that exposure entirely.
RAG Implementation
Practical guides to building Retrieval Augmented Generation systems that ground your AI in real-time, verifiable knowledge.
Building a Production RAG Pipeline: Start to Finish
From document ingestion and chunking strategies to retrieval ranking and response generation, a complete walkthrough of building RAG that works.
Hybrid Search: Combining Semantic and Keyword Retrieval
Why pure vector search is not enough, and how combining semantic embeddings with BM25 keyword matching delivers significantly better results.
Measuring RAG Quality: Metrics That Actually Matter
Beyond simple accuracy scores, the evaluation metrics that predict whether your RAG system will succeed in production with real users.
Fine-tuning Guides
Learn how to adapt foundation models to your domain with practical fine-tuning techniques, from LoRA to RLHF.
LoRA Fine-tuning: Getting 90% of the Results at 10% of the Cost
A practical guide to Low-Rank Adaptation fine-tuning, including when to use it, how to prepare your training data, and common pitfalls to avoid.
Preparing Training Data: Quality Over Quantity
Why 1,000 high-quality examples outperform 100,000 messy ones, with a step-by-step process for curating domain-specific training datasets.
Evaluating Fine-tuned Models: Building Your Test Suite
How to create domain-specific evaluation benchmarks that measure what matters to your business, not just generic NLP performance.
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We publish new technical guides and case studies regularly. Book a strategy session to discuss your specific use case, or check back for our latest content.