AI & Agentic Automation

It will, occasionally — so nothing consequential happens without a check. Low-confidence results route to a person, actions that touch money or stock need approval, and everything is logged. AI is used where a mistake is cheap and catchable, not where it is expensive.

That is the only sensible way. One workflow, built end to end, measured against how the task runs today. If it does not clearly beat the current process, we stop — and you have spent very little finding that out.

Almost never. Existing models plus your own data and good engineering around them covers nearly every business use case. Training a custom model is expensive, slow, and rarely the actual constraint.

For some tasks, yes — reading documents, drafting, classifying, answering questions from your own data. For others, a plain rule-based workflow is more reliable and far cheaper. The skill is knowing which is which, and building guardrails and human checkpoints wherever a mistake would be costly.

Only if you choose that, and only where it is appropriate. Where data sensitivity requires it, workflows can be built so that confidential data never leaves your systems. We decide this deliberately, before anything is built.

For connecting tools and automating workflows, n8n is faster to build, easier for your team to understand, and much easier to change later. I write custom code where the logic genuinely needs it — but reaching for code first usually just makes the automation harder to maintain.