Workflow Automation

Who this is for

• Teams copying data between tools by hand
• Operations running on a fragile chain of spreadsheets
• Companies whose Zapier bill exceeds what it saves
• Businesses with a repetitive judgement task an AI step could absorb

What you get

• Working automations with error handling and retries
• Failure alerting, so a silent break is not possible
• Documentation your team can maintain without the developer
• AI steps where a task needs judgement rather than rules
• A run log showing what executed, when, and what it produced

Automate the process, not the symptom

The first question is whether a step should exist at all. Plenty of manual work is a workaround for a missing integration or a bad data model, and automating it faithfully preserves the original problem while adding a system to maintain. Mapping the process before building usually removes steps rather than automating them.

Failure handling is the whole job

An automation that works is easy. One that fails correctly is the engineering. APIs go down, rate-limit, and return unexpected shapes; a step will eventually receive input nobody anticipated. Retries with backoff, dead-letter handling for anything unprocessable, and alerting on failure are what separate a reliable automation from a silent data-loss machine.

Visible runs, not a black box

Your team needs to see what ran, what it did, and what it produced — without asking a developer. N8N's execution history covers much of this, and where a custom workflow is warranted the run log is built in. An automation nobody can inspect gets distrusted, and distrusted automation quietly gets replaced by the manual process it replaced.

AI for the judgement steps only

Most automation is deterministic and should stay that way — rules are cheaper, faster and auditable. But a genuine judgement step, like classifying an inbound message or extracting fields from an unstructured document, is where a language model absorbs work no rule can. Put the model on that step and leave the rest deterministic.

Claude and MCP inside the workflow

Language-model steps run inside the same pipelines: the Claude API for classification, extraction and drafting, and MCP-connected tools where an AI step needs live access to your systems rather than a stale copy of their data. The division of labour stays strict — deterministic steps remain deterministic code, model steps return schema-validated output the next step can trust, and every AI decision lands in the run log so a human can audit why the workflow did what it did.

How the engagement works

1. 01

   Discovery

   Map the process as it actually runs, including the exceptions people handle by hand. That is usually where the real cost is hiding.

2. 02

   Design

   Agree the workflow, the failure behaviour and who gets alerted when something breaks, before anything is built.

3. 03

   Build

   Implement in N8N where your team should own it, in code where the logic warrants it, with retries and alerting from the first version.

4. 04

   Ship

   Hand over with documentation and a run log your team can read, plus 30 days of support while it settles into real use.

Technology

Orchestration

N8N when the client should be able to see and adjust the flow themselves; custom Node.js or Python when the logic exceeds what a visual builder expresses cleanly.

Integration

REST and GraphQL APIs, webhooks, scheduled jobs, and database triggers.

AI steps

LLM calls with schema-validated output for classification and extraction, so a downstream step can rely on the shape.

Reliability

Retries with backoff, dead-letter queues, idempotency keys, and failure alerting to email or Slack.

Proof

Frequently asked