Insights
Product, design, AI, and engineering perspectives from our team.
Scaling AI Pilots: From Proof of Concept to Production
Most AI pilots never make it to production due to the significant gap between controlled proof-of-concept environments and production reality. This guide explores the four key gaps that prevent scaling and provides a structured approach to bridge them.
AI Model Monitoring in Production: What to Track and How to Alert
Production AI models fail silently through accuracy drift, data changes, and performance degradation. Learn what metrics to track, how to set up effective alerts, and compare monitoring tools to catch problems before they impact your business.
Technical Debt and AI: Building Intelligence on Solid Foundations
Technical debt creates specific barriers to AI adoption that compound over time. Legacy systems struggle with data quality, rigid architectures, and unreliable integrations that break when AI components are introduced.
Real-Time Data Pipelines: When You Need Them and When You Don't
Real-time data pipelines process data with minimal latency, but most mid-market businesses don't actually need them. Understanding when batch processing suffices versus when streaming is truly required can save significant complexity and cost.
dbt for Mid-Market: Data Transformation Without Enterprise Costs
dbt brings enterprise-grade data transformation capabilities to mid-market Australian companies without enterprise costs. Learn how to implement modern data pipelines using SQL and software engineering best practices.
Data Quality for AI: Why Garbage In Still Means Garbage Out
Poor data quality is the fastest way to turn a promising AI project into an expensive failure. Learn how to assess if your data is AI-ready and implement a practical framework for data quality: profiling, validation, monitoring, and remediation.
Building Your First Data Pipeline: A Guide for Business Leaders
A data pipeline is an automated system that moves data from various sources, transforms it, and delivers it where your business needs it. Understanding data pipelines is crucial for business leaders because they form the foundation for reporting, analytics, and AI initiatives.
Data Warehouse vs Data Lake vs Lakehouse: Which One Do You Need?
Choosing the right data architecture — warehouse, lake, or lakehouse — can make or break your AI initiatives. Each approach serves different needs and impacts your ability to build AI-powered features.
Microservices vs Modular Monolith: Choosing the Right Architecture
Not every application needs microservices. For many mid-market companies, a modular monolith delivers clean architecture benefits without distributed systems complexity. Here's how to choose the right approach for your team in 2026.