Repetitive work, done without hands.

Cost structure and ownership. Zapier and Make bill per execution or per automation, which becomes expensive fast once a workflow runs tens of thousands of times monthly. n8n is open-source and self-hostable, so you pay for your compute and storage once, not per workflow run.

Equally important: n8n workflows export as JSON and live in your git repo. If you ever want to move off n8n, the logic is yours. Zapier and Make workflows are harder to extract because they live inside their platforms.

That said, Zapier is excellent for simple triggers across SaaS tools when execution volumes are low, and Microsoft Power Automate makes sense if you are heavily invested in the Microsoft 365 ecosystem. We will recommend them when they fit.

AI is reliable for the right jobs and unreliable for the wrong ones. Classification, extraction from text, drafting messages, summarization: typically 95-99% accuracy with good prompting on current models. Autonomous decision-making without human review: not production-ready, not from us.

Every AI step we deploy has three things: a documented confidence threshold, a human review path for low-confidence outputs, and observability so you can see what the AI decided and why. If an AI step starts behaving badly, you will know, and the workflow has a non-AI fallback.

n8n is open-source with a healthy enterprise sponsor, but vendor risk is real for any tool. Our hedge is: workflows are architected as a sequence of steps, not n8n-specific primitives. The actual business logic is portable to any workflow engine with similar primitives (Temporal, Prefect, Dagster, custom Python).

Our deployments also keep the integrations themselves (API calls, data transformations) in reusable code modules, not locked inside n8n node configurations. If we had to port a client off n8n, it would take weeks of work, not a full rebuild.

Three options, we help you pick what fits:

Option 1: You do. We write documentation and train someone on your team. Our involvement ends. Most common for teams with existing engineering capacity.

Option 2: Retainer. Fixed monthly engagement, we handle maintenance, fixes, and small changes. Larger changes go through a scoped project.

Option 3: Hybrid. You own routine maintenance, we are on-call for incidents and larger changes.

Yes, that is often where automation delivers the most value. Approaches, in order of preference:

If an API exists: we use it. Custom integration code where no off-the-shelf connector exists.

If a database is accessible: direct read or write with careful scope. Change data capture if ongoing sync is needed.

If only a UI exists: headless browser automation with Playwright. We are honest that UI automation is brittle: it works, but it needs maintenance when the UI changes.

In our experience, no, for a specific reason: the people who understood the manual process end up owning the automated version and extending it. Their job shifts from doing the task to monitoring the automation, handling edge cases, and designing the next improvement.

What automation does change is the composition of the work. If someone was spending 30 hours a week on data entry, that time goes to higher-leverage work. That is a hard conversation to have before starting, and we help clients think through it.