Data Science & AI Services

Most regulated operations hold data they cannot pass to a public model provider: customer records, transaction history, decision logs and internal policy. Sending that out for training or inference can be a regulatory event in itself, yet it is exactly the data an assistant or a decisioning model needs to be useful.

We design for the boundary from the start: self-hosted or private-endpoint models where capability allows, retrieval patterns that keep your data on your infrastructure while a strong model only reasons over what was retrieved, and contractual limits on any third-party endpoint. The boundary is the design constraint, not a finishing detail.

Our WislaSearch pattern is one example: retrieval over your internal documents, databases and APIs, run on-premise with no external data transfer. See it in production in the AI search assistant case.

A lot of AI work fails because the technique was chosen before the problem was understood. We start from the decision the business needs to make, the data behind it and the bar it has to clear, then pick the simplest approach that clears that bar. As a rough guide:

• A rules engine where the logic is known, stable and must be exactly auditable.
• Classical ML where the task is prediction over structured data, such as scoring or ranking.
• A language model, built on generative AI, where the input is language: documents, queries, conversations.
• A hybrid where a model proposes and deterministic rules constrain what it can decide.

Where the question is really analysis rather than a model, that is data analytics. The simpler choice is not a compromise. It is usually cheaper to run, easier to explain and easier for your team to own after handover.

AI and ML work carries more uncertainty than a standard build, so we structure the commercial model around proving value early. A proof of concept on your own anonymised data is a fixed-price, time-boxed piece with a clear question to answer: does the model clear the bar your use case needs.

If it does, production work follows as time and material or an outcome-based arrangement, with governance, monitoring and audit logging built in from the first sprint. If the proof of concept says the data or the use case is not ready, you have spent a small fixed amount to learn that, instead of a large one to discover it late.

An AI system that only the vendor understands is a liability, so we structure the engagement so your people learn it while it is being built. Our named lead works in the open with your data owners, your compliance function and the engineers who will run the model, and the reasoning behind each design decision is written down.

Knowledge transfer is continuous, not a final phase. By handover your team has already operated the system, and the questions that usually surface after a vendor leaves have been asked while we were still in the room. You own the source, the pipelines, the model artefacts and the documentation.

Not every question needs a model. A lot of the value in a regulated operation comes from data science in the older sense of the term: understanding what the data already says. A cohort analysis that explains why a segment leaves, a forecast that sizes next quarter's load, an experiment that settles whether a change actually moved the number.

We treat that as the same discipline as model building, because the honest answer to “should we train a model here” is often “analyse the data first”. Where the goal is to understand rather than predict, our data analytics service is the right entry point, and we will say so rather than sell a model you do not need.

It is also the cheapest way to find out whether a larger AI investment is worth making. A short, honest look at your data shows what is learnable before you fund a full build.