AI automation · Make and Zapier Make agency: Make and Zapier automation experts for business

What we do

• Build: scenarios in Make and Zaps in Zapier for workflows such as lead routing, document handling, notifications, reporting and data sync between tools.
• Review and rescue: an existing set of scenarios that has grown, breaks without anyone noticing or depends on one person who has left. We document what it does, fix errors, remove duplicates and add monitoring.
• Add AI steps: classification, extraction and drafting inside a scenario, with validation and an approval step.
• Monitor and maintain: alerts when a run fails, regular checks of connections and changes when your tools change.

Make, Zapier, n8n or custom code?

There is no best tool, only one that fits the workflow. The table compares the points that decide it.

Costs depend on how many runs your workflow produces. We estimate the volume with you before recommending a tool, so that nothing surprises you later. Our article n8n agency or DIY and the guide to self-hosting n8n in the EU cover the alternatives in more detail.

What makes an automation reliable

The difference between a demo and a system is everything that happens when something goes wrong.

1. Error handling: every step that can fail has a defined reaction: retry, alert or stop.
2. No duplicates: a run that is repeated must not create a second invoice or a second contact. We build checks on identifiers into the flow.
3. Rate limits and timing: apps limit how fast they accept requests. The flow respects that and queues instead of failing.
4. Logging and alerts: failures reach a named person by email or chat, not a log nobody reads.
5. Clean connections: credentials are kept in the platform’s connection store, with the minimum permissions each flow needs.
6. Names and documentation: scenarios, modules and variables have names that say what they do, and a short document describes each flow, its owner and what to do when it breaks.
7. Test before live: changes run on test data first, and live flows are not edited in place.

AI steps inside Make and Zapier

Both platforms can call language models. We use them for classification, extraction and drafting, always with guardrails. The model returns structured output, a validation step checks it against rules and uncertain cases go to a person. Anything that leaves the company, such as an email to a customer, waits for approval. Emails and documents can contain hidden instructions aimed at the model, so the AI step gets only the permissions it needs, as described in the OWASP Top 10 for LLM Applications. More on building agents under how to build an AI agent.

Data protection

Automation platforms process your data and often keep an execution history. Before a flow goes live we check which data it handles, which provider processes it and where, and whether a data processing agreement is in place (Art. 28 GDPR). We also check how long execution histories are kept and whether the personal data in them can be reduced. For transfers outside the EU, the provider must offer a valid mechanism, and you can check whether a US provider takes part in the EU-US Data Privacy Framework. Where data location is critical, we use n8n on servers in the EU. Art. 4 of the AI Act asks for sufficient AI literacy among staff, and chatbots must say that they are AI (Art. 50). This overview is general information, not legal advice.

Typical use cases

• Lead and enquiry routing: forms and emails classified, saved to the CRM and assigned, see CRM automation.
• Documents: attachments read, checked and filed, see intelligent document processing.
• Reports: data from several tools collected into a regular report, see AI reporting.
• Notifications and approvals: a chat message with an approve button before an invoice or an email goes out.