We take companies through AI implementation, from the process audit to the point where the team manages on its own. No grand transformation talk — we start with the tasks that are actually costing you time.
Implementing AI in a company is not buying a tool. It is changing how a particular piece of work gets done, and it has four parts: identifying the processes worth automating, matching solutions to those processes, connecting them to the systems you already run, and teaching the team to use them day to day.
Skipping any one of those is the most common reason implementations fail. The company buys licences, a handful of people use them after a month, and nobody does after a quarter. We have seen the difference between “using AI” and building AI into how a company works — in the first case around 10% of tasks reach AI, in the second productivity rises several times over.
It pays off fastest where the work is repetitive and can be described:
Honestly: sometimes it is better to wait. If a process is undocumented and looks different every time, put it in order first — automating a chaotic process just produces chaos faster. If your data is scattered and nobody owns its quality, start there. AI will not fix a mess in your data; it will reproduce it.
We work in four stages. After each one you can stop and judge the results before committing to the next — we do not ask for a decision on the whole programme up front.
We look at how the work happens today: which tasks take the most time, where the bottlenecks form, and which of them can be automated. The output is a ranked list of candidates, ordered by effect against cost.
We present a plan matched to what your team can absorb, separating quick improvements from projects that need integration. For each item we state what you need to prepare and what to expect.
We build, connect to your systems and go live. Where off-the-shelf products fall short we build bespoke solutions — automations, AI agents and MCP servers.
We train the team on what has been built and appoint internal “AI Champions” to carry it forward. The goal is a self-sufficient company, not a permanent dependency on us.
The scope follows the audit, but in practice AI implementation usually comes down to a few recurring areas:
The full list of services is on the home page.
Most failed implementations we have seen fell over on the same four things. None of them is a technical problem.
We do not publish a price list, because the cost depends above all on how much integration a process needs and whether an off-the-shelf tool can carry it. We quote after a free consultation and an initial review — a number before that would be guesswork.
Four things drive the cost:
After the consultation we present the plan in three variants, so you can pick the scale:
We have worked in companies where an AI transformation genuinely happened. On software projects we achieved a threefold increase in productivity, and in office work roughly fourfold. Where the implementation was comprehensive and finished with training, productivity rose by 300–400%.
For the avoidance of doubt: those are results from implementations run end to end, not from making tools available. With a “buy the licences and see” approach, the real share of work reaching AI stalled at around 10%. The difference is not the technology — it is whether somebody took the company through the change.
The scope of data, the legal basis for processing and where processing takes place are settled before anything goes into production — not afterwards. That includes whether a provider may use your data to train models; in enterprise offerings this can usually be turned off, but it has to be configured deliberately.
The duty to take measures towards an adequate level of AI literacy among the people using these systems (Article 4 of the AI Act) has applied since February 2025, and most of the regulation's remaining provisions have applied since August 2026. We factor that into the training plan, and cover it in more depth on the AI Act readiness audit page. If you want to know how we process the data of visitors to this site, see our privacy policy.
With a process audit, not with choosing a tool. First we establish which tasks take the most time and recur regularly enough to be worth automating. Only then do we match technology to those tasks.
The first working improvements (Quick Wins) usually go live within weeks, because they build on off-the-shelf tools. A pilot project that needs integration with your systems runs to months. A full transformation is a continuous process, delivered in stages.
Not in the way we work. We plan implementations so the company can go through the change without cutting roles: staff whose tasks are largely taken over by AI are given new ones, including maintaining and overseeing the AI itself. That is a deliberate choice — we do not want this industry to be associated with redundancies.
That depends on the service and its configuration, and it is one of the first things we settle in a project. Enterprise providers usually offer modes in which your data is not used for training. We agree the scope of data, the legal basis and where processing happens before anything goes into production.
No. Many implementations are configuration and integration of existing tools. An in-house technical team helps with bespoke solutions, but even then we can build them and hand them over to your team with documentation and training.
That is the most common situation we meet, and it is rarely the tool that is at fault — it is the absence of a process and of training. We start by auditing what you already have and working with the team. Buying more licences changes nothing.
A free consultation, after which we present a plan matched to your processes — in several variants, so you can choose the scale.