Insights
Practical perspectives on software, AI, and automation from the team at Automathing.
ISQ 2025 data: Québec is not behind on AI adoption, it is behind on intent. An analysis of the numbers and what they mean for businesses here.
Applied AI explained in practical business terms: what it is, what separates it from demos, where it creates value, and how to adopt it without creating operational risk.
RAG isn't a universal solution, it's a precision tool. How to decide if your project needs it, how it actually works, and how to avoid the mistakes that break it.
A company can succeed technically at AI and still fail to create value. The biggest risks are organizational, human, and strategic. Here is what leaders need to watch.
An AI agent doesn't crash: it hallucinates, loops, picks the wrong tool, and still returns 200 OK. How we built observability for our agents' reasoning.
Full autonomy is a risk on critical actions. How to add human-in-the-loop checkpoints to your AI agent workflows, with a real example from TowerZ.
Before launching any AI project, answer these 4 strategic questions: inefficiencies, team alignment, target outcomes, and data governance.
Multi-agent coding architecture at Automathing: one orchestrator per project, specialized AI coding agents, and rules written from real errors.
A practical framework for quantifying the business value of AI-driven automation: beyond cost savings, across speed, quality and capacity.
