AI Automation

Most AI automation projects fail for an unglamorous reason: someone automated the process that was easiest to describe rather than the one that was costing the most. So we start by measuring. We sit with the people doing the work, time the tasks they repeat, and count how often each one runs. A task that takes four minutes and happens two hundred times a month is worth more than one that takes a full day but happens twice a year — and teams consistently guess this wrong, because the painful task is memorable while the frequent one has become invisible.

We also tell clients which processes not to automate, and this is genuinely part of the service. Low-volume work with high variation usually costs more to automate than it saves. Processes that are about to change because a system is being replaced should wait. Anything where a mistake is expensive and hard to reverse needs a human approval step, which reduces the saving enough that it sometimes is not worth doing. An agency that says yes to every item on your list is optimising for its own invoice, not your return.

The most important technical decision on any build is which steps need AI and which do not. Language models are extraordinary at reading messy input, classifying intent, extracting fields from unstructured text, and drafting language. They are unreliable at arithmetic, at following long chains of exact rules, and at doing precisely the same thing every time — the properties you most want in a business process. So we use AI at the edges where input is messy, and deterministic code in the middle where correctness matters. Putting a model in charge of a calculation your finance team relies on is how automation projects lose trust in their first month, and trust is very hard to win back.

Every automation we build assumes it will encounter something we did not plan for, because it will. Rather than letting it guess confidently and be wrong, we build in a confidence threshold: below it, the item is routed to a person with the reasoning attached and the automation stops. This is deliberately conservative at launch. As the logs accumulate and you can see the actual accuracy on real volume, we raise the threshold and hand more of the work over. Earning autonomy with evidence is slower than switching it on, and it is the difference between an automation your team relies on and one they quietly stop using.

We build on infrastructure you control, usually n8n or plain code running in your own cloud account, rather than on a closed AI automation platform. Two reasons, both practical. Per-task platform pricing looks cheap in a pilot and becomes your largest line item at production volume, precisely when the automation is working well. And when your team can open a workflow, read it, and change a rule themselves, they will keep improving it. When every adjustment requires a ticket to us, it stops being adjusted, and it drifts out of sync with how the business actually operates.

Adoption is a people problem more often than a technical one. An automation that removes a task somebody has owned for six years will be resisted if it appears without warning, and quiet resistance kills more deployments than bugs do. We involve the people currently doing the work in defining how the automation should behave — they know the exceptions nobody documented, and they are the ones who will spot it going wrong first. In practice, the automations that stick are the ones where the person who used to do the task now supervises it.

Measurement continues after launch, because the interesting number is not what we projected. We record baseline metrics before automating — time per item, error rate, volume, backlog age — and track the same metrics afterwards. Some automations beat the estimate. Some come in under it because a step turned out to need more human review than expected. You get the real figures either way, and they inform which process is worth doing next far better than any proposal we could write in advance.