AI and ML development services

AI and ML development for a regulated business is not the same problem as for an open consumer product. The model has to live inside the compliance boundary, the training data has to respect retention and consent, and the decisions have to be explainable to a regulator and to the person asking why the answer was no.

We build AI and ML for decisioning, anti-fraud, route and operations optimisation, and internal assistants over your own data. The constraint is the same across them: the AI runs inside the boundaries your compliance function already maintains, not outside them.

That constraint shapes the engagement from the first call. The questions that decide whether a model can ship are governance questions, where the training data came from, how a decision is logged, what the model does on the edge cases, and we answer them at the design step rather than after a regulator asks.

Three patterns decide whether a regulated AI build reaches production, and none of them is about the model architecture on its own.

What matters most:

• Explainability is chosen at the model-class step, not bolted on. A model that cannot be explained the way the regulator expects cannot ship.
• Data quality drives the outcome. We start with a data audit because a small early investment there saves a large later one.
• AI does not replace integration. An assistant that cannot reach the right data is a chatbot; a fraud model that cannot reach the payment stream is a research paper.
• Governance is the deliverable, not a report. Audit logs, override paths, challenger models and an exit drill are designed in from the start.

On a route-optimisation engagement for a European pharma distributor, a proof of concept on six months of anonymised data delivered 30% less travel time and 20% more visits per day in three weeks, before any full build began.

Where the model needs live data, that work sits next to our API integration services; for the broader fintech platform, see financial software development.

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 language 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, so staff get useful answers without a single record leaving your walls. The same shape applies to a support assistant or an internal knowledge tool, where the value is in the private data and the risk is in letting it out.

WislaSearch in production: see the AI search assistant case.

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 the governance, monitoring and audit logging built in from the first sprint rather than retrofitted. 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.

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. Sometimes that is a language model; often it is not. 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 where the input is language: documents, queries, conversations.
• A hybrid where a model proposes and deterministic rules constrain what it can decide.

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. When a large model genuinely is the right tool, the same logic applies in reverse: we say so, and we design the boundary it has to run inside.

An AI system that only the vendor understands is a liability, so we structure the engagement so that 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, not carried in someone's head.

What we need from your side is specific rather than large: someone who can grant data access and explain how the data was produced, a compliance contact who can say what an auditor will ask, and an engineer or two who will own the system later. The work proceeds without them, but slower and with more rework, so we name them at the scoping step.

Knowledge transfer is continuous, not a final phase. Documentation, runbooks and review sessions happen as the system takes shape, so by handover your team has already operated it and the questions that usually surface after a vendor leaves have been asked while we were still in the room.