Start with the single process that leaks the most money, not with a tool. Map how that process actually runs today, find where the work is redone or dropped, and build a small system that removes those specific failures. AI is what makes that build affordable and fast enough to be worth doing — weeks rather than a year — but it is the method, not the goal. The goal is operational excellence: a process that runs the same way every time, without a person holding it together.
Every growing company arrives at this question the same way. Revenue is real, the team is stretched, and the cracks are visible to everyone — the renewal that slipped because nobody owned the handoff, the pre-sales work redone three times, the customer data living in four systems that never quite agree. Someone says the word “AI”, and the conversation immediately becomes about tools.
That is where it goes wrong.
Why most AI-in-operations projects stall
They start with the technology and work backwards to a problem. A tool is selected, a pilot is run, a demo impresses people, and then the thing quietly fails to change how anyone works on a Tuesday. The reason is nearly always the same: the underlying process was never described, so there was nothing concrete for the tool to improve.
The second failure mode is scope. An “AI transformation programme” is announced, a year of work is scoped, and the operational pain that triggered it is still there eighteen months later. Growing companies do not have a year. The festival date, the renewal cycle, the funding round — none of them move.
Both failures share a cause. AI was treated as the objective. It is not. Operational excellence is the objective. AI is simply the reason it is now affordable to build software shaped around your business rather than bending your business around software.
Start with the process that leaks money
Pick one process. Not a department, not a strategy — one process, with a beginning and an end, that you already suspect is costing you. In most growing companies it is one of three.
The three places it usually is
- Renewals. Nobody owns the moment a contract comes up. It is tracked in someone’s calendar, chased late, and occasionally missed entirely. Illustratively, this is the kind of process that can carry around €45,000 a year of avoidable waste.
- The sales-to-delivery handover. What was promised in the sale is re-discovered by the delivery team, and the customer explains their situation twice. Around €97,000 in an illustrative case.
- Pre-sales work. The same scoping, the same estimates, the same answers, rebuilt for each opportunity because nothing was captured the first time. Around €84,000.
Those figures are illustrative, drawn from separate scenarios rather than added together — the point is the shape, not a total. What matters is that each one is a specific, boundable process, which is exactly what makes it a candidate for a first build. A vague ambition to “use AI across the business” is not.
If you cannot describe the process on one page, you are not ready to automate it — you are ready to map it.— the honest first step
What gets built, and why it is small on purpose
The output of a first engagement is not a platform. It is a point solution: a small system that does one operational job properly, for the people who do that job. A customer success portal exists so customer success managers can actually manage the post-sale relationship — see which accounts are drifting, act before renewal, and stop churn that would otherwise arrive unannounced. A partner portal exists so partners can self-serve instead of emailing someone. Each is named by the operational outcome it delivers, not by the software category it belongs to.
Underneath sits the foundation every engagement includes: an admin console and the automation engine that runs the work, bundled as one build module. You can see what those point solutions look like in practice, and how the automation engine underneath them works.
Small is deliberate. A narrow system that removes a real failure is worth more than a broad one nobody adopts, and it can be delivered in weeks — which means you find out whether it worked while the problem is still the problem.
What it costs and how long it takes
The first step is an Assessment, scoped to the processes we are looking at. It produces a written description of how the process runs now, where it fails, and what building the fix would involve. You pay that fee only if you decide not to proceed — go ahead with us and it costs you nothing.
The price is 30% of the first-year saving we measure and prove, invoiced in equal monthly instalments across the engagement — so the cost lands alongside the value rather than after it. When the work is complete you receive the full source code and own it outright, at no extra charge.
The reason those numbers are public at all is that most of this industry hides them. The full pricing is here, and the engagement runs through six defined steps from the first call to source-code handover.
Do you hire someone, or buy a tool?
This is the question underneath the question, and the honest answer is that the usual options each leave the same gap open.
A full-time operations executive designs the plan but does not write the code, so the strategy lands as a document and waits behind product for engineering time — at a cost of more than €200,000 a year. More SaaS is fast to buy and cheap to start, but you bend your process to the tool and pay forever, on a stack that can easily exceed €150,000 a year. A development agency can build, but needs a finished specification handed to it — which assumes the operational thinking is already done. And doing nothing feels free, which is precisely why it is the most expensive option on the list.
What actually closes the gap is having the operational judgement and the build capability in the same place, so that the description of the process and the software that runs it are never handed between two parties who each understand half of it.
Common questions
Do we need an AI strategy first?
No. You need one described process and a decision about which failure in it is worth removing. A strategy that is not anchored to a specific process tends to produce documents rather than change.
How long before we see anything working?
Weeks, not months — which is the whole reason AI tooling matters here. A first point solution is deliberately narrow so it can be delivered, used, and judged quickly.
What if our processes are a mess and not written down anywhere?
That is the normal starting condition, and it is what the Assessment is for. Nothing needs to be documented before you begin; the mapping is part of the work.
Do we own what gets built?
Yes. The source code transfers to you for a fixed one-time fee per build module, and after that there is nothing to keep paying in order to keep using it. That is a deliberate contrast with a subscription you can never stop.
Is our data going to leave the EU?
No. Systems are built and hosted on EU infrastructure, which for most European operators is a requirement rather than a preference.