What we learn building workflows and AI systems for growing businesses. Practical, measured, written to be useful.
Pricing models, learning curves and control compared, with a worked cost example. Which platform fits how your team works?
Read the article →Connectors and MCP plug AI directly into your email, drives and CRM. The three risks that creates, and the controls that make connectors safe to love.
Read the article →n8n templates, MCP servers and GitHub projects are brilliant and risky in equal measure. Five plain-language checks before anything touches production.
Read the article →Prompts, workflows and AI skills are now real company assets, and most of them live in personal accounts. One home, an owner, review and history.
Read the article →The models are cheap and getting cheaper. What you pay for is diagnosis, adoption, governance and measurement. An honest answer, including when not to hire one.
Read the article →From uncontrolled personal use to on-premise. A practical framework for matching each AI workflow to the right deployment level, plus the legal questions to ask.
Read the article →Seven traps that quietly drain a growing team's capacity, and the fixes that recover it. Most teams are leaking more hours than a new hire would add.
Read the article →MIT found 95% of enterprise GenAI pilots fail to deliver ROI. The cause is rarely the technology. The five layers that decide the outcome, and where to start.
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