When we searched YouTube for "AI automation agency client results", the top six videos were all about how to start one or how to win its first clients. Some of the agencies on your shortlist may have started out that way. What you want to avoid is paying for a Zapier-plus-GPT build with no logs, no tests and no owner once the builder has moved on, and finding out the first time a customer gets a wrong email.
Below is the vetting we would want a buyer to run on us: six questions for any AI automation agency in the UK, the answers to listen for, and the cases where you should not hire one at all.
Six questions that separate builders from resellers
A demo shows that something worked once. Ask these questions of every agency on your list and write down the answers.
| What to ask | A good answer | Red flag |
|---|---|---|
| Show me an agent you built that is in production now. Can I speak to that client? | A named production system that has run for months, and a client happy to talk | Only demos, Loom recordings or a course certificate |
| Who maintains it after launch, and what happens when an API or prompt breaks? | A named owner, monitoring, and a defined fix path through a retainer or support terms | "It just runs", or a handover with no support |
| What gets logged, and where does a human approve or review? | Every run logged; a person approves anything customer-facing or irreversible; clear stopping conditions | Fully autonomous from day one, no logs |
| Where does our data go? Who is controller and who is processor under UK GDPR? Will you help with a DPIA? | The data flow mapped, processor terms signed, help with the DPIA | Customer data pasted into personal tool accounts; no DPA |
| Who owns the code, workflows, prompts and the tool and API accounts? | You do, and it is built in your accounts | Built in the agency's Zapier, Make or OpenAI account, so you rent it |
| What is the success metric, and what happens if you miss it? | Agreed at kickoff, measured, with a consequence if missed | "Efficiency" or "transformation" with no number |
On data, the bar is set by the regulator. The ICO's guidance on AI accountability says that in the vast majority of cases AI processing will trigger the legal requirement for a DPIA, and that "you cannot delegate these issues to data scientists or engineering teams." Any agency that waves this away is leaving the risk on your desk.
For the production question, you can read how our LinkedIn prospecting system grew from Make.com workflows into n8n and then a full platform, with the build written up.
The agents that make money are boring
A builder on r/AI_Agents who said they had built more than 25 agents wrote a post on which ones actually earn. By the poster's account they were the dull ones: an email-to-CRM updater, an FAQ agent, a parser. The poster also said the buyers were ops managers, not technical founders. Answering a sceptical commenter, the poster summed it up: "The value isn't the prompt it's the implementation, reliability, and ongoing ownership."
Anthropic's engineering team arrived at the same place from the model side. In its guide to building agents it writes that the most successful implementations use simple, composable patterns rather than complex frameworks, and it advises "finding the simplest solution possible, and only increasing complexity when needed."
So skip the agency pitching a multi-agent swarm. Good AI automation services pick one process your team repeats every day, make it reliable, log every run and give it an owner.
How we run a build
- Free 30-minute audit. You bring one process. We ask where it starts, where the data ends up and who checks it today. The audit is how a custom project starts.
- Written proposal within 48 hours. One fixed price, the integrations involved and one success metric.
- Build. The first agent goes live 21 days from kickoff.
- Metric check. If we hit the agreed metric, the project is done. If we miss it, we rebuild free or refund the build fee.
- Optional ongoing work. Some clients take the agent and run it themselves. Others keep a fractional AI team to tune it and add the next one. Our average client tenure is 14 months.
We work remotely with SMEs in London and across the UK, so you do not need an AI automation agency in London to get one that knows UK GDPR and UK business tooling.
Pricing, plainly
- Custom project: fixed price excl. VAT, quoted after the free audit and confirmed in writing within 48 hours. The scope sets the number, so we do not publish a bracket.
- Fractional AI team: £600 per day (excl. VAT) for a team of 3-4 people. A typical retainer is 3 days a week, which is £1,800 a week or about £7,800 a month.
Those are the only two prices we quote, and both exclude VAT. The full detail is on how our pricing works.
Works with your stack
An AI agent development company should be able to name the field its agent writes to. These are the systems we build on and what the agent actually does in each.
- HubSpot. Can write call notes and extracted fields to the contact or deal. A UK HubSpot partner on r/hubspot said HubSpot is "often poorly set up", so check the data hygiene before any agent starts writing to it.
- Salesforce. Can log activity and update opportunity fields, and a person signs off before anything changes a critical process.
- Pipedrive and Microsoft Dynamics 365. Can create or update deals, activities and notes.
- Microsoft 365, Teams, SharePoint and Copilot Studio. If you live in Teams, Copilot Studio is the right front end. We build the parts it struggles with behind it as connectors, using Power Automate and Azure Functions: heavy data processing, external APIs, memory and version control. A builder on r/copilotstudio described the same pattern: they moved heavy processing into Azure Functions exposed as HTTP connectors.
- Slack. Can post approval requests to a channel so a person clicks yes before an email or update goes out.
- Xero. Can read invoices and bills for reconciliation and reporting.
- n8n, Make and Zapier. The triggers, retries and routing between systems. We default to n8n or Make for ops work.
Human review is part of the design
In an r/salesforce thread after Dreamforce '25, one commenter described the new Agent Builder as "an automated flow with explicit before and after actions and a prompt in the middle". Salesforce's own relaunch points the same way. SiliconANGLE reports that Agent Script lets enterprises define "complex, deterministic workflows" such as following refund policies or qualifying leads.
We build the same way. A fixed flow handles the steps that must never vary, and the model does the reading and drafting in the middle. A person approves anything that sends money, changes a contract or emails a customer for the first time. Our post on a human-in-the-loop review queue shows what that queue looks like in practice.
Agency, freelancer or in-house hire
| Freelancer | In-house hire | Quantum Automations | |
|---|---|---|---|
| Production references | One person's past work | None for your business until they ship something | Written-up builds in our portfolio |
| Cover after launch | A single point of failure, with no cover during leave or illness | One employee; the knowledge leaves if they do | You, or a fractional team, optional |
| Who owns the accounts | Whatever you agree in writing | You | Agreed in the proposal |
| Cost before the first agent | A day or project rate | Salary plus recruitment before month one | Fixed price after a free audit |
If you are seriously weighing a hire, our breakdown of the cost of hiring an AI developer versus an agency runs those numbers. If you want advice before committing to a build, an independent AI consultant may be the better first step.
Who this is not for
- You need one simple workflow inside one tool. Zapier pitches its own builder as a way to create agents "in minutes" across thousands of apps, and for a single rule in one system that may be all you need. Try it first.
- You want to learn to build agents yourself. We build agents for clients and have no course to sell.
- You have no measurable process to automate yet. In the government's AI adoption research, "a lack of identified need for AI" was among the most commonly cited barriers. If that is you, the audit will probably say wait.
- You want fully autonomous agents running critical processes with nobody checking them. We will not build that.