AI automation
AI automation agency: take the repetitive work off your team's desk
We connect your tools and add a language model only where a step needs judgment, such as reading a messy email or sorting a request. Everything else stays plain, predictable automation.
In short
AI automation links your apps with workflows that run on their own, using a language model for the steps that need reading or judgment. Typical jobs are sorting inbound email, extracting data from messages, updating a CRM, drafting replies and sending alerts. We design the flow, build it on n8n, Make, Zapier or code, test it and hand it over.
Where AI belongs in an automation, and where it does not
Most of a good automation is not AI. A trigger fires, data moves, a record updates. The model is one step in the middle, doing something a fixed rule cannot, like deciding whether an email is a quote request or a complaint.
| Step type | Use | Why |
|---|---|---|
| Move data between apps | Plain automation | Predictable and free of model errors |
| Read free text and classify it | Model | Wording varies too much for rules |
| Pull fields from messy documents | Model plus validation | Needs a check on each extracted value |
| Calculate, apply a policy, compare dates | Code or rules | A model can slip on arithmetic |
| Write a first draft of a reply | Model, person approves | Tone and facts need a human look |
If your task is mostly the first and fourth rows, you may not need AI at all. See AI automation vs RPA for the difference.
Do the arithmetic before you build
We calculate the time saved on a sheet before writing any workflow. Here is the method, with made-up numbers to show it.
| Line | Example value | How to get yours |
|---|---|---|
| Runs per week | 120 inbound requests | Count last month and divide |
| Minutes per run by hand | 5 | Time ten real cases |
| Share the automation handles fully | 70 percent | Check against 50 past cases |
| Minutes still needed for the rest | 2 per case | Review and exceptions |
| Hours saved per week | (120 x 0.7 x 5 + 120 x 0.3 x (5 - 2)) / 60 = 8.8 | Fill in your own figures |
Compare the saved hours with the build and running cost. If it pays back inside a year, it is worth doing. If not, we say so and you keep your money. Our AI automation ROI calculator does the same sum.
Tools we build on
We choose the tool for the job, not the other way round.
- n8n for complex flows, self-hosting and data you want to keep in your own environment. See n8n automation.
- Make for visual flows that a non-developer can read and edit.
- Zapier for quick connections between common apps.
- Custom code when a flow needs speed, scale or logic that no-code tools handle poorly.
Our head-to-head notes are in n8n vs Make.
Want AI automation done for you?A short call, a written plan and a fixed quote.
Get a proposalHow a project runs
- Pick one processWe start with a single workflow with clear volume, not a company-wide plan.
- Map it as it really runsWe sit with the person who does it and write down every step, exception and workaround.
- Build with a test modeThe flow runs on copies of real cases first. Nothing is written to your live systems until you approve.
- Add failure handlingRetries, a dead-letter list for anything the flow could not process, and an alert to a named person.
- Go live and watchThe first two weeks are supervised. We tune prompts and rules from what we see.
- Hand overWorkflow exports, credentials list, a one-page description and a short training call.
What you get and how long it takes
- Working automations in accounts you own, not locked inside ours.
- A map of the process before and after.
- A log of runs and failures that your team can read.
- Notes on what personal data passes through which tool, for your privacy review.
A single workflow with one or two integrations often goes live within a few weeks. Several connected workflows, or flows that need cleanup of old data, take longer. The quote gives dates for your scope.
How we report, and our nearest real work
You get a short note each week during the supervised period: runs, failures, items sent to a person, hours saved against the sheet above. Later, the same figures are in the run log whenever you want to look.
We have no published AI automation case study yet. The closest real project is Aura Realty, where we built a custom CRM so enquiries from the website, Instagram, WhatsApp and calls land in one place with follow-ups tracked. It shows the plumbing that automation needs before any model is added.
Updated 30 October 2026
Questions, answered
What should we automate first?
Will automation send wrong things to customers?
Do you replace our existing Zapier or Make flows?
Where does our data go?
What happens when a model or app changes?
Keep exploring
AI automation vs RPA: the real difference
RPA repeats steps someone recorded. AI automation reads, classifies and drafts. Most real processes need both, in a clear order.
Free toolAI automation ROI calculator
Enter how often a task happens, how long it takes and what staff time costs. The calculator returns the monthly saving, the payback period and the…
Servicen8n automation agency: workflows that keep running
We build n8n workflows for lead handling, reporting, onboarding and AI steps, and we add the retries and alerts that make them safe to leave alone.
Name the task that eats the most hours
Tell us on the contact page and we will run the arithmetic with you on a short call.