AI workflow automation for the admin your team repeats every day

Traditional workflow automation follows fixed rules, while AI automation adds language-model steps for inputs those rules cannot read. A rule can create a contact when a form is submitted. It cannot decide which team a free-text complaint belongs to or find an invoice number in a scanned PDF. Most workflows we build are mainly rules with one or two AI steps, because deterministic steps are cheaper, faster and easier to test.

We use n8n by default because it can be self-hosted, handles complex branching and code steps, and carries no usage-based platform fees when you run it yourself. That keeps costs predictable as volume grows and keeps run data inside an environment you control. Zapier or Make can still suit a few simple, low-volume flows owned by a non-technical team, and we recommend them when they are the better fit.

Yes. A status change such as an order dispatched, an appointment confirmed or an invoice falling due can trigger a WhatsApp message through the official Business API. Because these messages usually start outside the 24-hour customer service window, each one uses a template approved by Meta, with variables such as name, order number or date filled from your system. Delivery and read statuses are written back to the CRM record, and an opt-out is recorded so later workflows skip that contact.

In most cases, yes. n8n has built-in nodes for widely used tools such as HubSpot, Pipedrive, Zoho CRM, Xero, QuickBooks Online, Shopify, WooCommerce, Gmail, Google Sheets and Slack, plus a generic HTTP Request node for any system with a REST API. When a tool offers neither, we write a small Node.js or Python connector or work from scheduled exports. We check each system's API limits and permissions during discovery, before quoting.

Self-hosting suits organisations that want data to stay in their own cloud account, expect high volumes or need custom nodes, while n8n Cloud suits smaller teams who would rather not run a server. When we self-host, we deploy n8n in Docker inside your own cloud tenancy on AWS, Azure or Google Cloud, with a PostgreSQL database, backups, encrypted credentials, an update routine and uptime alerts. We compare both options and their running costs during discovery.

The run pauses and a person decides. An AI step returns structured fields, and the workflow checks them against your data, for example that an order number exists or a category is on the allowed list. Anything that fails a check, or that the model marks as unclear, goes to a review queue in email, Slack or a simple form. Each reviewer correction is logged, and those corrections become test cases for the next prompt change.

Each failure sends an alert to a named person with the workflow name, the failed step and the error message. In n8n we attach a dedicated error workflow that posts these details to email, Slack or WhatsApp, and temporary faults such as timeouts are retried automatically first. Failed executions are stored, so once the cause is fixed the same data can be re-run rather than re-entered. A scheduled check also flags workflows that have gone quiet when they would normally be busy.

Build cost depends on the number of workflows, the systems involved and how much AI processing is needed, and it is quoted as a fixed price once discovery is complete. Running costs have three parts: the n8n Cloud plan or the server for self-hosting, model usage for AI steps, which is billed per token, and any per-message charges, such as Meta's fees for WhatsApp template messages. We estimate each part at your expected monthly volume before you approve the build.

As an indicative range, a small, well-defined workflow is often live two to four weeks after discovery, while larger processes are delivered in stages. The main drivers are how many systems are involved, the quality of their APIs, the number of exceptions and whether AI steps need an evaluation set. Routing website leads into a CRM with a WhatsApp follow-up sits at the simple end; order-to-invoice flows across departments take longer.

Yes. We start by listing every existing automation: its trigger, the account it runs under, the app connections it uses, who relies on it and how often it fails. Common causes are expired connections, renamed fields, filters that no longer match and plan limits being reached. For each one we then recommend repairing it where it is, merging it with similar flows or rebuilding it in n8n, and we add alerts and documentation either way.

Only the fields each step needs, because we design every workflow around data minimisation. Personal details are removed or masked before AI steps that do not require them, and model calls run on business API terms under which your inputs are not used for training. In a self-hosted n8n, credentials are encrypted with a key you hold, saved execution data is pruned after an agreed period and successful runs can be set not to store data at all. Workflows are designed with UK GDPR in mind.

Use a workflow when the steps are known in advance, and consider an agent only when the path changes from case to case. A workflow with AI steps is predictable, cheaper to run and easier to audit, so it covers most operational processes. An agent that plans its own steps suits open-ended tasks such as researching a lead across several sources. We usually start with a workflow and add an agent later if the exceptions justify it.

Yes, and we set things up so they can do it safely. Simple edits such as new email wording, an extra field or a different recipient can be made in the n8n editor by a trained member of staff. We keep a staging copy for testing, export workflow definitions to version control so changes can be compared and rolled back, and write a runbook for each workflow. If you prefer, we handle changes and n8n upgrades under a support agreement.