How to choose between prompting, RAG, and fine-tuning
Three ways to make an AI feature work, three very different price tags. Here is the decision framework we use to pick the right one, written for the person signing off on the budget.
Tag: ai-and-automation
Expert in the loop: rethinking AI oversight
Human in the loop made sense when AI errors were obvious. The failures we see now look like working software, and only an expert can tell the difference.
How to Prepare Your Company's Data for AI Automation (Without Creating a Security Nightmare)
Before your company can use AI, your data has to be clean and secure. Here is how we help businesses organise their messy databases and API silos so they can safely deploy AI agents without accidentally exposing confidential data.
Google's Gemma 4 is Apache 2.0, and That Changes the Local Model Conversation
Gemma 4 ships under Apache 2.0 with native audio, vision, reasoning, and function calling. Here is what it means for teams running local models and building AI-powered products.
What happens when you let an AI agent loose on your production infrastructure
A developer lost 2.5 years of data in seconds after Claude Code ran a Terraform destroy on live infrastructure. Here is what actually happened, and why the same mistake is easier to make than you think.
Measure Twice, Prompt Once: Why Business Requirements Come Before the Code
AI coding tools like Cursor and Replit are fast, but without rigorous business logic and security protocols, they build liabilities. Here is how we bridge the gap.
