AI Integration & Automation
We identify where AI can make work better, then build the workflows and tools that make it happen.
Ask about AI Integration & Automation Book a free consultation
Prefer to write? Send us an email at [email protected]
Who it’s for
Teams with repetitive, time-consuming work, such as documents, content, catalogues or data handling, who want to know where AI genuinely helps and where simpler automation is enough.
It may be for you if
- The same documents, emails or data are copied, checked and retyped every week.
- A process depends on one or two people who know how it works.
- Errors appear at the hand-offs between tools, and nobody notices until later.
- You have tried automation before, and it broke when the inputs changed.
01What you get
What you get
- A map of the workflow as it runs today, with the steps worth automating marked
- A recommendation for each step: AI, ordinary rule-based automation, or a person
- The automated workflow, and the screens people use to review and correct its output
- Documentation of what the automation does and where it can go wrong
02In detail
What it involves
An automation project changes how a piece of work gets done. We plan it with the people who do the work, connect it to the systems you already use and leave you with something your team can run and check.
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Process mapping
Every step of the process as it runs today: who does it, how long it takes, which systems are involved and where errors or rework appear.
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The right approach for each step
Rule-based automation where inputs are predictable, AI-assisted steps where they vary (free text, inconsistent documents), and a person wherever judgment or accountability matters.
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Integration with your systems
The automation works with the tools you already run, such as email, spreadsheets, document stores or business systems, through the connections they offer. Where a connection isn’t possible, we say so before the work starts.
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Review built in
People see what the automation did and why, and can correct it. Corrections are visible, so the team can judge how well the process is working.
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Handover and documentation
A description of what the automation does, what it depends on and where it can go wrong, so it can be maintained and changed later.
03Why it pays off
Give your team back the hours that repetitive work takes.
Repetitive work is rarely anyone’s job description, yet it fills a large part of the week. Automating the right steps frees that time for the work people were hired to do, without giving up control of the result.
In most teams, routine handling of documents, content and data happens in the gaps: between systems that don’t talk to each other, in spreadsheets maintained by one person, in emails forwarded for approval. It is slow, it is easy to get wrong, and it grows only by adding people.
Generative AI changes which of those steps can be automated. Rule-based automation has always handled predictable inputs; AI can now help with the untidy ones too, such as free text, inconsistent documents or first drafts. In a preregistered experiment published in Science, professionals using ChatGPT for writing tasks finished in 40% less time, and their work was rated 18% higher in quality.
The benefit lasts only when people can trust the result. That is why review is built into every workflow we deliver: people see what the automation did and can correct it. Speed comes from automating the repetitive part; quality comes from keeping judgment where it belongs.
Because the automation connects to the tools you already run, there is no new platform to learn and no data moved without a reason. And because it is documented, it doesn’t become a black box that only its builder understands.
Time back for skilled work
Hours spent copying, sorting and retyping move to work that needs your team’s experience.
Fewer errors at hand-offs
Steps that depended on someone remembering run the same way every time, and exceptions are flagged.
Growth without more routine
A larger volume of work no longer means proportionally more manual hours for the same routine.
Control stays with people
Review points are part of the design, so you decide how much is automated and can see how well it works.
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Fig. 1
40% less time taken on professional writing tasks by people using ChatGPT, with the quality of their work rated 18% higher.
Participants whose work was weaker at the start gained the most.
About this figure: 40%
A preregistered online experiment with 453 college-educated professionals, published in Science in July 2023. The tasks were short, occupation-specific writing tasks; a real workflow also includes steps that AI does not speed up.
Source: Noy & Zhang, Science, Experimental evidence on the productivity effects of generative artificial intelligence (external site), 2023
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Fig. 2
+15% more customer issues resolved per hour by support agents with access to an AI assistant.
One study at one software company found different effects depending on workers’ experience.
About this figure: +15%
The study involved 5,172 customer-support agents. Less experienced workers improved speed and quality; the most experienced saw small speed gains and small quality declines. This is a measured result in one setting, not a general rule.
Source: Brynjolfsson, Li & Raymond, Generative AI at Work (published in The Quarterly Journal of Economics, 2025) (external site), 2024
These figures come from studies and rules in other settings. They show why the work matters, not what will happen in your business.
04How we work
How we work
Map the process
Every step, who does it, how long it takes, and what goes wrong.
Choose the right tool
Rules where the inputs are predictable; AI where they are not; people where judgment matters.
Build with review built in
Automation that shows its work, so people can check and correct it.
We start with the work, not the model. We map how a process actually runs today, find the steps that are repetitive, slow or error-prone, and decide where AI helps, where ordinary automation is enough, and where a person should stay in charge.
Then we build it: the workflow, the connections to the systems you already use, and the interface people use to review, correct and trust the output.
Questions?
Have questions? Let’s talk.
Not sure whether this fits your situation? Tell us briefly what you’re working on. We’ll read it, ask a few questions and suggest a sensible next step, even if that step isn’t working with us.
- A short description of the problem is enough to start.
- No sales script and no obligation.
- You talk to one of the founders, not an account manager.
Ask about AI Integration & Automation Book a free consultation
Prefer to write? Send us an email at [email protected]
05FAQ
Frequently asked questions
Will the automation run without people?
Not where judgment matters. We automate the repetitive steps and keep a person in the loop wherever a decision or accountability is involved. We don’t promise fully hands-off automation.
Do we have to change our systems?
Usually not. The automation works with the tools you already run, such as email, spreadsheets, document stores or business systems, through the connections they offer. Where a connection isn’t possible, we say so before the work starts.
How do we know the automation is doing the right thing?
People see what it did and why, and can correct it. Corrections are visible, so the team can judge how well the process is working.
Who maintains it afterwards?
You receive documentation of what the automation does, what it depends on and where it can go wrong, so it can be maintained and changed later, by your team or with our help.
Which tasks are worth automating?
Steps that are repetitive, slow or error-prone, typically in handling documents, content, catalogues or data. Mapping the workflow shows which ones they are.
How do you decide between AI and ordinary automation?
Rule-based automation where the inputs are predictable, AI-assisted steps where they vary, such as free text or inconsistent documents, and a person wherever judgment or accountability matters.
Do you build custom AI models?
No. We connect established AI services and tools to the way you already work. This isn’t training or building custom AI models.
What do people work with day to day?
The automated workflow and the screens they use to review and correct its output. They see what the automation did and why.
Can we start with one process?
Yes. Mapping one workflow and identifying where AI is worth applying is a typical first piece of work.
How much does it cost?
It depends on the scope. After a first conversation we describe what the work would include and what it would cost, in writing, before anything starts.