AI.

AI systems that run in production, not just in demos.

We build AI systems that run on your own data: question answering over your documents (RAG), agents that automate processes, and LLM features embedded in your existing software. A typical pilot reaches production in 6–8 weeks.

What we do

Most AI projects die at the demo stage. The reason is rarely model choice — it is that they never met production reality: real data is messy, real users ask unexpected questions, and real cost shows up on the first invoice. We build with those constraints from day one.

With Retrieval-Augmented Generation (RAG) we build question-answering systems over your own documents, contracts, and knowledge base. The model does not invent facts; it answers from your material and shows its source.

AI agents run multi-step workflows rather than returning a single answer: classify a request, pull data from the relevant system, apply the rule, write the result. In agent architecture the hard part is not model intelligence but what happens when something fails — we design for that first.

We embed AI features into your existing product: smart search, summarization, content generation, classification. A provider-agnostic abstraction layer means your code stays put even when the model changes.

Who it's for

Organizations sitting on a large document archive they cannot search
Teams that ran an AI pilot but never shipped it
Software companies adding AI features to their product
Operations teams with manual, rule-based, repetitive processes

How we work

01
Discovery and feasibility
We examine your problem and your data. The output is not a proposal but an honest answer: can AI solve this, with which approach, and if not, why not.
1 week
02
Pilot
A narrow but end-to-end working version, tested with your real data and real users. Success criteria are defined numerically up front.
4–6 weeks
03
Production
Scalable architecture, monitoring, cost control, security hardening, and rollout. With AI systems, the real work starts here.
4–8 weeks
04
Iteration
Accuracy and cost tuning driven by usage data. AI systems are not built and left alone; they are measured and adjusted.
Ongoing

What you get

A working system — source code and infrastructure definitions included, fully yours
An evaluation set: automated tests that measure the system's accuracy
A cost model: token and infrastructure spend per usage
Architecture documentation and handover training

Frequently asked questions

How long does an AI project take?: Feasibility takes 1 week, a working pilot 4–6 weeks, and production rollout 4–8 weeks — so typically 2–3 months to first real use. Data quality shifts this: if your data is scattered, data preparation becomes the longest phase.
Should I choose RAG or fine-tuning?: RAG, in the large majority of cases. Fine-tuning does not teach a model new facts; it teaches behaviour and format. If your knowledge changes often, you need to cite sources, or your data volume is large, RAG is the right answer. We add fine-tuning when a specific tone, language, or output format is required.
Will our data leave our infrastructure?: That is your decision and we design the architecture around it. Three options: a cloud model under an enterprise agreement (data not used for training), cloud hosting in the EU region, or an open model running on your own servers. For regulated data we frequently use the third.
Which model do you use?: We do not commit to a single one. The architecture is model-agnostic: the provider layer is abstracted and the model becomes a configuration value. When a better or cheaper model appears six months later, one line changes rather than your codebase.
What does an AI project cost?: Two lines: build and run. Build is a project fee that scales with scope. Run is per-usage token cost plus infrastructure, and we produce a concrete monthly estimate during feasibility — no surprises later.

All services

IntegrationWe fix the place where your systems stop talking to each other.ConsultingNotes that say what to start and what to postpone.

Let's start

A short intro call is enough. We'll scope it together from there.

Get in touch

What we do

How we work

Discovery and feasibility

We examine your problem and your data. The output is not a proposal but an honest answer: can AI solve this, with which approach, and if not, why not.

1 week

Pilot

A narrow but end-to-end working version, tested with your real data and real users. Success criteria are defined numerically up front.

4–6 weeks

Production

Scalable architecture, monitoring, cost control, security hardening, and rollout. With AI systems, the real work starts here.

4–8 weeks

Iteration

Accuracy and cost tuning driven by usage data. AI systems are not built and left alone; they are measured and adjusted.

Ongoing

Frequently asked questions

How long does an AI project take?

Feasibility takes 1 week, a working pilot 4–6 weeks, and production rollout 4–8 weeks — so typically 2–3 months to first real use. Data quality shifts this: if your data is scattered, data preparation becomes the longest phase.

Should I choose RAG or fine-tuning?

RAG, in the large majority of cases. Fine-tuning does not teach a model new facts; it teaches behaviour and format. If your knowledge changes often, you need to cite sources, or your data volume is large, RAG is the right answer. We add fine-tuning when a specific tone, language, or output format is required.

Will our data leave our infrastructure?

That is your decision and we design the architecture around it. Three options: a cloud model under an enterprise agreement (data not used for training), cloud hosting in the EU region, or an open model running on your own servers. For regulated data we frequently use the third.

Which model do you use?

We do not commit to a single one. The architecture is model-agnostic: the provider layer is abstracted and the model becomes a configuration value. When a better or cheaper model appears six months later, one line changes rather than your codebase.

What does an AI project cost?

Two lines: build and run. Build is a project fee that scales with scope. Run is per-usage token cost plus infrastructure, and we produce a concrete monthly estimate during feasibility — no surprises later.