Related project: Deckent, our agent orchestration project
The most valuable use lies in the structured data an organisation already owns. Order, production, costing and procurement data sits in the ERP in an orderly form, so the model does not have to guess; it reads the number from the system and interprets it. This is why quotation preparation, request classification, procurement comparison and report interpretation return results fastest.
Most AI projects die at the demo stage. The cause is not model choice but never having met production reality: real data is dirty, real users ask unexpected questions, and real cost shows up on the first invoice. We build the work under production constraints from the start.
With Retrieval-Augmented Generation (RAG) we build question answering over your own documents, contracts and knowledge base. The model does not invent information from outside; it takes its answer from your records and shows which one it relied on.
AI agents carry out multi-step workflows rather than returning a single answer: classifying a request, pulling data from the relevant system, applying the rule, and writing the result. In agent architecture the real question is not the model's intelligence but what happens when it fails. We design for that from the start.
Instead of direct access we work within authorisation and security boundaries: only as much data as the user and the process need enters circulation. For features embedded into your existing product we use a provider-independent abstraction layer, so your code does not change when the model does.
We examine your problem, your process and your data. The output of this stage is not a proposal but an honest answer: can this be solved with AI, if so with which approach, and if not, why.
A narrow version that nevertheless works end to end. Tested with your real data and real users. Success criteria are defined numerically from the start.
Scalable architecture, authorisation, monitoring, cost control, security hardening and rollout. In AI systems the real work begins here.
Accuracy and cost improvement driven by usage data and logs. AI systems are not built and abandoned; they are measured and tuned.
Data quality determines the timeline more than model choice does. If your data is orderly and accessible the first working version arrives quickly; if it is scattered, data preparation becomes the longest stage. Once the scope is clear, we share the project schedule and its duration.
In the large majority of cases, RAG. Fine-tuning does not teach a model new information; it teaches behaviour and form. If your information changes often, if you need to cite sources, or if your data volume is large, RAG is the right answer. We add fine-tuning when a specific language, tone or output format is required.
That is your decision and we shape the architecture around it. There are three options: a cloud model under enterprise API terms (data is not used for training), cloud hosted within the European region, or an open model running on your own servers. For data under data-protection regulation we often use the third. Which data goes where is settled in writing at the start.
We build the system accounting for it. The source of an answer is shown so the user can verify it; cases below the confidence threshold are handed to a human; accuracy is measured with regular evaluation tests. The error margin does not reach zero, but it becomes manageable and measurable.
We do not tie ourselves to one model. The architecture is model-independent: the provider layer is abstracted and the model becomes a configuration value. When a better or cheaper model appears, a single line changes rather than the code.
There are two components: build and run. The build is a project price set once the scope is clear. The running cost is per-use model spend plus infrastructure, produced as a concrete monthly estimate from expected volume.
Our notes related to this service.
AI projects that handle personal data have three architectural options: mask the data before it reaches the model, use an enterprise cloud hosted in a European region, or run an open model on your own infrastructure. The choice is a trade-off between cost and risk, and which data goes where should be put in writing at the start of the project.
In most enterprise projects the right answer is RAG. Fine-tuning does not teach a model new knowledge; it teaches behaviour and format. Use RAG when your information changes, when answers must cite a source or when your document volume is large. Add fine-tuning when you need a fixed tone, format or domain language.
Consulting that sees the process on the floor and builds it in the system.
We fix the place where your systems stop talking to each other.
A short intro call is enough. We'll scope it together from there.