ML development services
Custom machine learning models trained, deployed and kept healthy - predictive, recommender and risk models, with the MLOps to run them in a regulated environment.
Results from work we have shipped
An AI-driven solution that optimises medical sales representative routes and improves field efficiency, using deep learning over visit patterns to predict the best routes.
Shortlisting a software development partner?
The questions to ask in an RFP, three warning signs a vendor may not deliver, and a scoring matrix to compare contractors objectively.
ML we build
Demand, churn, lifetime value and risk over your historical data, with the validation to trust the numbers.
Ranking and next-best-action models for products, content and operations.
Transaction and behavioural models with a feature store and real-time scoring. The applied fraud product lives on AI development; here we build and tune the models behind it.
Underwriting and risk models built to be validated, monitored and explained.
Models that cut travel time and raise visits per day for field and logistics operations.
Deployment, monitoring, drift detection, retraining and a model registry, so a model survives contact with production.
The data plumbing that decides model quality - built once, owned by your team.
How we deliver ML development?
The same delivery discipline on every engagement - from the first map to a handover your team runs.
We check the data you have, its quality and its governance, because a small early investment there saves a large later one.
We build the feature engineering and the training data so results are reproducible.
We train, validate and challenge the model against the bar your use case needs, with the evidence written down.
Deployed in your cloud with drift detection, a retraining cadence and monitoring on the model's own targets, handed to your team.
What shapes the work
A model is only worth building if a decision changes because of it. We start from that decision, the data behind it and the bar it has to clear, then build the simplest model that clears the bar and the pipeline to keep it there.
In a regulated setting the modelling and the governance are one job. Where the data came from, how a prediction is logged, how the model is validated and challenged - these are designed in, not added when an auditor asks. Where the question is really analysis rather than a model, that is data analytics.
Most models degrade quietly. The data shifts, the world moves, and a model that was accurate at launch is wrong six months later without anyone noticing. So we treat monitoring, drift detection and retraining as part of the deliverable, not an afterthought - with a model registry and a cadence your team can run alone. Where the model needs live data, that work sits next to our API integration services.
We start with a data audit because the quality of the training data decides the quality of the model, more than the choice of algorithm does. If the data is not ready, a short, honest audit tells you that before you fund a full build. Where the goal is to understand the data rather than predict from it, our data analytics service is the better fit, and we will say so.
A proof of concept on your own anonymised data is fixed-price and time-boxed, with one question: does the model clear the bar. If it does, production follows as time and material or an outcome-based arrangement, with monitoring and retraining built in from the start. The full commercial approach is on the Data Science &
In a regulated setting a model you can explain often beats a model that scores a shade higher. A logistic regression or a gradient-boosted tree whose reasoning a risk officer can read will clear more approvals than a deep network that cannot, even when the network looks better on a benchmark.
So we default to the simplest model class that clears your bar, and reach for something heavier only when the data genuinely calls for it – unstructured inputs, very high dimensionality, patterns a linear model cannot hold. The heavier model then has to earn the extra cost in monitoring, explainability and compute.
That discipline also makes a model cheaper to run and easier for your team to own after handover, which in a long-lived risk or scoring system usually matters more than the last decimal of accuracy.
How is ML development different from AI development?
ML development is the modelling work: training custom models on your data, building predictive and recommender systems, and running the operations that keep them accurate in production. AI development wraps a capability into a product, covering agents, vision, natural language, decisioning and the integration around them. Most regulated engagements use both, and we will tell you which one your case needs rather than sell the larger of the two.
Do you build the model only, or run it in production as well?
Both. Deployment, monitoring, drift detection, retraining and a model registry are part of the default handover, because a model that is accurate at launch and unwatched is wrong within months while nobody notices. Where that operations layer is the work you actually need, on its own or on models your team already runs, it is scoped as MLOps development.
What kinds of models do you build?
Predictive and forecasting models, recommender systems, anomaly and fraud models, credit risk and scoring models, and optimisation models for operations. The class is chosen against the decision you need to change: a well specified statistical forecast frequently does the same job as deep learning at a fraction of the complexity, and we say so before the budget is committed.
Our data is messy. Can you still help?
Yes, and the data audit comes first for exactly that reason. It tells you what is usable, what is missing and what would have to be fixed before a model can be trained, from a small fixed piece of work instead of a large one. For a European institution it is also the compliance question: Article 10(3) of the EU AI Act, Regulation (EU) 2024/1689, requires the training data behind a high-risk system to be relevant, sufficiently representative and, to the best extent possible, free of errors and complete. Credit scoring is one of those high-risk uses, and the Digital Omnibus on AI, Regulation (EU) 2026/1744, moved the application date for standalone Annex III systems to 2 December 2027, which is time to fix the data rather than a reason to defer it.
How does adding machine learning to a legacy system change the timeline?
The model is rarely the long pole. Reaching the data is: an older core often has no clean API, the history sits across several systems that define the same field differently, and reconciling them is what sets the date. We scope the data access path during the audit and give you a staged plan, so the first useful model trains on a subset of sources instead of waiting for the whole estate to be tidy.
How do you know a model will hold at production volume?
We build for production from the first sprint instead of promoting a notebook later. The same feature pipeline feeds training and inference so results do not drift between the two, and load, latency and failure behaviour are tested against your real volumes before release. Every model, dataset and metric is versioned and each prediction is logged with the version that produced it, which makes a degradation traceable rather than a surprise.
Should we build machine learning capability in-house or bring in a specialist team?
An internal team can own this, and for a bank with a standing data function that is usually the right end state. A specialist shortens the parts that are easy to get wrong and expensive to retrofit: the feature pipeline, validation and challenger models, monitoring, and the governance a European supervisor will ask about. We build inside your repositories and your cloud alongside your engineers, so the capability stays with you when the engagement ends.
How do you keep models explainable and compliant?
We choose model classes and logging patterns that produce explanations by default, validate against a challenger where a regulator expects one, and design override paths so a person can intervene with the reason recorded. In the EU an automated credit decision also answers to Article 22 of the GDPR, Regulation (EU) 2016/679, which has applied throughout and did not move when the AI Act timetable did. A decision the model cannot explain is a decision you cannot ship, so explainability is designed in and not reported at the end.
Who owns the model and the pipeline at the end?
You do. Source, feature pipelines, training scripts, model artefacts and documentation are yours, running in your own cloud and handed over with a runbook. Nothing about operating or retraining the model requires us to still be there.
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Have a prediction worth making?
Bring the decision and the data behind it. We will audit the data and scope a proof of concept that answers whether a model clears your bar.