Blog
Practical AI & data insights
Real lessons from building data infrastructure and AI systems for multi-location operations. No theory — just what works.
Why Your AI Project Will Fail Without Clean Data
80% of AI projects fail — and it's almost never the AI's fault. The real killer? Dirty, disconnected, inconsistent data. Here's what that looks like in practice.
Building Your First Data Pipeline: From POS to Business Intelligence
Your POS system, scheduling tool, and inventory tracker all have valuable data. The problem is they don't talk to each other. Here's how to connect them.
AI Agents That Actually Work: Monitoring Your Business 24/7
Forget the hype about AGI. Real AI agents are already running production monitoring, catching errors, and alerting operators — here's how we built ours.
The Data Warehouse Decision: BigQuery, Supabase, or Both?
Choosing where to store your data is one of the most consequential early decisions. We've run both BigQuery and Supabase in production — here's when to use each.
From Spreadsheets to AI-Ready: A Practical Migration Guide
Every data-driven company started with spreadsheets. Here's a practical, step-by-step guide to migrating off manual processes without disrupting operations.