Independent & Luxembourg-based

AI Consultancy in Luxembourg

Practical AI for small organizations: SMEs, NGOs and solopreneurs. Sovereign by default, EU-hosted, open-source, and yours to own. From the first honest conversation to a system that runs every week without you.

Book a pre-discovery call

01 – Scope

What an AI consultancy actually does for a small Luxembourg team

Most of the work is not model selection. It's the unglamorous part before and after: finding which of your recurring tasks is genuinely mechanical, working out what data that task needs and where it currently lives, deciding what must never leave EU jurisdiction, and then building something narrow enough to finish and reliable enough to leave running.

A good AI consultant in Luxembourg should be able to tell you, early and without invoicing you for the privilege, where AI does not fit. Plenty of small-team problems are better solved by fixing a spreadsheet, changing a process, or writing forty lines of ordinary code. Recommending that is part of the job.

What you should expect at the end: a working system, the code and credentials in your hands, documentation a non-technical colleague can follow, and a clear number showing what changed. Not a slide deck.

02 – Fit

Who this is for

Three kinds of organization, all with the same underlying advantage: small enough that we can understand the whole picture before writing a line of code.

01 SMEs Ten to fifty people, a handful of workflows that eat the same hours every week, and no appetite for an enterprise-scale commitment. Start with one workflow, measure it, expand.
02 Solopreneurs One person doing the work of four. Orchestration lets you keep the parts only you can do and hand the repeatable parts to a system that runs overnight.
03 NGOs & human rights organizations Limited budget, sensitive data, and often a genuine adversary. Sovereignty is not a preference here; it's the difference between a safe system and a liability.

03 – The differentiator

Sovereignty-first, and you own what we build

We use the best model for the job, including non-European ones, but the architecture around the model is sovereign. Data sits on EU-hosted infrastructure. Processing that touches personal or confidential material runs on European or self-hosted open-weight models. The tooling is open source, so nothing depends on a vendor's pricing decisions two years from now.

The model is a replaceable component. The architecture is where sovereignty actually lives, and it is far harder to retrofit than to get right at the start.

At the end of a project you hold the code, the infrastructure accounts, and the documentation. If you never speak to us again, the system keeps running. That is the test we hold ourselves to.

04 – Services

What we do, at a glance

AI Orchestration

Multi-step workflows that connect the tools you already use, run on a schedule, and hand you the exceptions.

Knowledge & Data Systems

Turn scattered documents, inboxes and shared drives into something your whole team can actually search.

Strategy & Advisory

Readiness assessments, tool selection, roadmaps and training, for when the decision comes before the build.

Ongoing Partnership

A fractional AI team member on retainer, embedded in your workflow rather than parachuted in.

05 – Jurisdiction

Why hiring locally still matters

Not for the coffee. For jurisdiction. GDPR determines what you may do with your data; the EU AI Act determines what you must disclose and document about the systems processing it. Both are answerable questions, but they need answering in the design phase, not after a system is live.

A consultant working from outside the EU will usually default to a US-hosted stack because it's the path of least resistance. That default quietly places your data under a foreign jurisdiction. For an SME that may be an acceptable risk. For an NGO handling case files, it is not.

Luxembourg also has its own texture: a dense concentration of finance and EU institutions at the top of the market, and a thin middle where most organizations know AI is relevant but have nothing running. We wrote about that gap at length in AI in Luxembourg.

06 – The honest comparison

Boutique or Big-4?

Deloitte, PwC, EY, KPMG and Accenture all run AI practices in Luxembourg, and they are good at what they are built for: large organizations, large budgets, long timelines. If you are a five-person NGO or a twenty-person SME, you are not their target customer. You will be quoted out of range, or fitted into a standardized package that does not match your actual workflow. That is structural, not a criticism.

Boutique consultancies exist for the middle of the market. We scope deeply because we can afford to; your data is small enough to understand completely, which is an advantage, not a limitation. We use open-source tooling because it is genuinely better for small teams. We stay involved after go-live because single-handover delivery does not work at this scale.

The tradeoff is honest: we cannot field twelve people next Monday, and we will say no to work outside our range. If you need a hundred-person transformation programme, hire the firm built for it.

07 – Starting

How to start

  1. A free pre-discovery call, twenty minutes. You describe the task that annoys you most. We tell you honestly whether AI is the right tool for it.
  2. Discovery. A proper look at your workflows, tools and data. You leave with a written, prioritized roadmap that is yours to keep, whoever builds it.
  3. Build one thing. Narrow enough to finish, measurable enough to prove. Then expand from a system that already works.
  4. Decide what happens next. Hand-off with documentation, or an ongoing partnership. Both are real options, and we will not pretend the second is mandatory.
Book a pre-discovery call

Common questions

Should I hire an AI consultant or figure it out myself?

Both can work. The deciding factor is whether you have an in-house technical person with a few hours a week to track an AI tooling space that changes weekly.

Both can work. The real question is whether you have time to learn the field while running everything else. The AI tooling space changes weekly. What's best practice in March is obsolete by July. If you have an in-house technical person who can dedicate a few hours a week to staying current, you can absolutely do this yourself. If you don't, you'll either pick the wrong tools, build something fragile, or spend so long evaluating options that nothing ships. We help organizations skip the evaluation tax and get to a working system faster.

When is the right time to bring in an AI consultant?

When you have a specific, recurring task that eats hours every week and feels mechanical. Or when you've tried AI tools yourself and hit a ceiling you can't get past.

The clearest signal is when you have a specific, recurring task that eats hours every week and feels mechanical when you do it. Not "we should probably use AI somehow." That's too early. "We spend six hours a week routing inbound emails to the right team, and it's always the same patterns." That's exactly the right time. The second signal is when you've tried AI tools yourself and hit a ceiling: the chatbot gets you partway, but the last twenty percent is too fiddly to maintain by hand.

What questions should I ask before hiring an AI consultant?

Four matter most: comparable past work, ownership of the final code, where your data sits and which jurisdiction can access it, and what ongoing support costs after delivery.

Four matter most. First: can they show you something they built for an organization roughly your size? AI work for a Fortune 500 looks nothing like AI work for a four-person NGO. Second: who owns the code and the system at the end? You should own it, full stop. Third: where does your data live during and after the project, and which jurisdiction can compel access to it? Fourth: what does ongoing support look like, and what does it cost? The system will need adjustments. If they vanish after delivery, the system goes stale within a year.

How much does an AI consultant cost in Luxembourg?

It depends on scope, but the honest range for a small organization is a few thousand euros for a first working workflow, not the six-figure engagements the large firms quote.

It depends entirely on scope, and anyone who quotes a number before understanding your workflow is guessing. What we can say is the shape of it: a first, tightly scoped workflow for a small team is a few thousand euros, not a six-figure programme. The pre-discovery call is free, and discovery produces a written roadmap you keep whether or not you build with us. Costs stay lower than the large-firm equivalent for two structural reasons: we scope precisely because your data is small enough to understand fully, and we build on open-source, self-hosted tooling rather than stacking per-seat SaaS subscriptions you pay for forever.

How do you measure success on an AI project?

In hours and outcomes, not technology. Agree what the current state costs before you start, measure the same thing after the system is live, and compare.

In hours and outcomes, not in technology. Before we start, we agree on what the current state costs you. Usually that's time: hours per week spent on the task, or backlog depth, or response time to a customer. After the system is live, we measure the same thing again. A successful project moves the number meaningfully and the system keeps working without constant intervention. If the AI is impressive but you still spend the same hours on the task, the project failed. We'd rather build something modest that you actually use than something ambitious that needs babysitting.