Automation between systems
The management system, the website, email and spreadsheets stop being islands. An order arriving on the site appears in the system without anyone retyping it.
Software & automation
Repetitive work should not be done by a person.
In every company there is somebody who, every Monday morning, downloads a file, pastes it into a spreadsheet, checks the numbers add up and sends an email. Those are hours going into a task a person should not be doing, and getting it wrong precisely because it is boring.
What we work on
It does not take a huge project. The most useful automations are often small, and pay for themselves within weeks.
AI integrations
Chatbots trained on your documents, intelligent search across archives, automatic data extraction from invoices and contracts. Applied where it genuinely helps, not to be able to say it is there.
APIs and synchronisation
Connections to the services you already use: couriers, payments, marketplaces, e-invoicing. Imports and exports that run by themselves, at night, without anyone remembering to.
Internal tools
Small programs built for one job, that somebody on your team uses ten times a day. They are not impressive, they save hours.
How to tell what is worth automating
Not everything deserves automating. The rule we use is obvious but it works: how much time the task costs in a year, against how much it costs to remove it for good.
An operation taking two hours a week is a hundred hours a year. An operation done once a month in ten minutes is not. The first pays for itself quickly, the second probably is not worth the trouble — and we tell you so, rather than selling you the automation anyway.
- Start with the most boring task. It is usually also the one with the most mistakes, so the gain is double: time and errors.
- Small jobs, fast return. Better five small automations that pay for themselves in a month than one year-long project.
- They stay visible. If something jams, somebody notices. A silent automation failing silently is worse than doing the work by hand.
- AI where it fits. Excellent for reading documents, summarising and classifying. Less suited where an exact, verifiable answer is required: we use it where its margin of error is acceptable.
How we work
A clear method,
zero surprises.
You always know where we are, what it costs and when it arrives. Every stage has a concrete deliverable you can see and approve before we move on.
- Hunting for wasteWe go through a typical week together and note every repetitive manual step. More of them turn up than expected, almost every time.
- Cost and benefitFor each one we estimate the hours it costs in a year and what it would cost to eliminate. Only the ones that pay for themselves survive.
- BuildingWe develop the automation and test it on real data, in parallel with the manual work, until we trust the result.
- HandoverThe manual work is switched off. We explain to people how it works and what to do if something jams.
- Watching over timeAlerts stay active: if an external service changes or something stops running, we know before it becomes a problem.
Small jobs, measurable return
Unlike a website or a management system, an automation has a return you can calculate with primary-school arithmetic: hours saved times hourly cost.
That makes it the kind of work where it is easiest to tell whether it is worth doing, and where we say no most often: if the sum does not add up, we tell you. Many jobs are small and take a few days of work.
As always the quote is fixed price, per individual job. No framework contract is needed: you start with one automation, see the effect on people’s time, and decide whether to carry on.
Frequently asked questions
The answers that actually matter.
Where should we start?
With the task you repeat most often and find most tedious. It is almost always the one with the most mistakes too, so the benefit is double. If you are not sure which it is, we find it together by looking at a typical week.
Do we have to change the software we use?
Usually no. The whole point of integrations is to make the tools you already have talk to each other, not replace them. Replacing only makes sense if what you use is itself the problem.
Is AI genuinely useful or is it a fashion?
It is useful for specific tasks: reading and classifying documents, answering recurring questions, extracting data from free text. It is less suited where an exact, verifiable answer is needed, because it can be wrong convincingly. We propose it where the margin of error is acceptable, and say so when it is not.
What happens if an automation breaks?
Automatic alerts flag the problem to us. And that is why, during handover, the automation runs alongside the manual work: until we trust it, nothing gets switched off.
What do you redo every week?
Tell us about the manual step that steals the most time. We do the sum and tell you whether automating it is worth it — including when the answer is no.
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