AI Automation Agency
We automate the work your team keeps doing by hand
Most automation projects fail for a boring reason: someone automated a process nobody had actually written down. So that’s not where we start. We start by watching the work happen. We sit with the people who do it, look at the real inbox, the real spreadsheet, the chat window with forty unread messages, and only then decide what a machine should take off their hands and what’s better left with a person.
What we build tends to fall into a few buckets. Chatbots that answer from your own documents. WhatsApp and voice agents that actually hold a conversation. Workflow automations that shuttle data between the tools you’re already paying for. Some of it leans on language models; plenty of it doesn’t, because when a task is predictable a plain rule and an API call is cheaper and far more reliable than asking a model to think about it.
You end up owning the lot: the working software in your own accounts, the code, the prompts, the documentation. Keep us on for changes if you want to. Nothing is wired up so that you have to.
What we build
Chatbots that know your business
Support and sales bots that answer from your help centre, product docs and the tickets you’ve already closed, and admit it when they can’t find the answer instead of bluffing.
WhatsApp AI agents
Agents on the official WhatsApp Business Platform that handle support, bookings, order updates and new leads right where your customers already are.
Voice agents
Phone agents that pick up, book, route and follow up, wired into your calendar and CRM so the call ends with something actually done.
Workflow automation
Lead routing, invoices, reporting, onboarding: the endless chains of copy, paste and “can someone double-check this?”
Internal assistants
Search across company documents, boil a long thread or a two-hour call down to the point, and draft the reply for a person to approve.
Document processing
Pull the fields out of invoices, forms, contracts and emails, and drop them straight into whatever system needs them.
How a project runs
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01
Find the work worth automating
We list the tasks: how often each one happens, how long it eats, what a mistake costs. Usually two or three jump out. The rest can wait.
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02
Prototype on your real data
Not a slide deck. A rough version you can actually poke at, built on your own emails, tickets and documents, so the problems surface now instead of after launch.
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03
Build with guardrails
Hard limits on what it can say and do, an obvious route to a human, logging for everything, and an alert the moment something breaks.
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04
Launch small, then widen
We put it in front of a slice of real traffic, watch closely, fix what we find, and only widen once the numbers hold.
When we’d tell you not to do this
- The task costs you an hour or two a week. A spreadsheet formula or an off-the-shelf template will handle it for nothing, so don’t pay us to over-engineer it.
- The process is still changing every month. Let it settle first, or you’ll be paying us to build the same thing twice.
- A wrong answer would be expensive (legal, medical, financial) and you want nobody checking it. We’ll build the human review step in, or we’ll walk away. We won’t ship that risk.
- You want a guaranteed resolution rate before a single thing has been tested. We don’t invent those numbers. We measure on your real conversations and tell you what we actually see.
Questions people ask us
What can actually be automated with AI?
The repetitive stuff that happens a lot and follows a pattern someone could describe out loud: answering the same questions, sorting and routing messages, pulling data out of documents, booking appointments, updating records, writing first drafts. Anything that leans on judgement, relationships or genuinely odd one-off cases is better left to people, with the automation doing the prep so they start further ahead.
How long does a project take?
A single workflow is often live in a week or two. A chatbot or agent wired into your systems usually runs three to six weeks, and most of that time goes on cleaning up content and testing against real conversations, not on writing code.
Do we need technical people on our side?
No. We just need someone who knows how the work gets done today and can answer questions while we build. The handover docs are written for non-technical people, and we record a walkthrough you can replay later.
Are we locked into one AI model or tool?
No, and we design it that way on purpose. The model sits behind a thin layer so we can swap it when a better or cheaper one shows up, which happens often. Your data, prompts and workflows stay in accounts you control.
What happens to our data?
We use provider plans that don’t train on your data, keep secrets out of the code, and give each part of the system only the access it needs. If the data genuinely can’t leave your environment, we run open-source models on your own infrastructure instead.
How do you know it worked?
We pick one or two things to measure before we start (response time, tickets closed without a person, hours saved a week) and check them against a baseline we take before launch. No baseline, no bragging.
Related services
Generative AI development
LLM apps, copilots and RAG on your data.
AI chatbot development
Bots that answer from your own documents.
WhatsApp AI agents
Support, orders and bookings on WhatsApp.
AI voice agents
Inbound and outbound calls, handled.
AI workflow automation
Remove the copy-paste between your tools.
n8n automation
Workflows that keep running after handover.
Tell us what’s eating your team’s week
A few lines about the task is plenty. We’ll tell you whether it’s worth automating and roughly what it would take, even when the honest answer is that it isn’t.