AI, machine learning and data science services for engineering

Artificial intelligence and data science in engineering cover regression and classification, time-series forecasting, anomaly detection, computer vision, signal processing, surrogate modelling, optimisation and decision support. The aim is not to apply the newest algorithm but to answer a research or technical question with defensible evidence. In practice, most of the effort lies in understanding the data, defining the target, designing validation and interpreting results. We support these stages and the writing of results for publication, and we do not promise accuracy levels that the data cannot support.

We begin with the decision or claim the model is meant to support and the cost of errors. We then assess feasibility: how many independent samples exist, how reliable the labels are, whether the operating conditions in the data cover those of interest and what baseline performance can be achieved by a simple method. Sometimes the right conclusion is that the data are not yet sufficient and that targeted data collection is the best investment.

This honest assessment prevents wasted effort and unrealistic expectations.

Raw engineering data often contain missing values, sensor faults, mislabeled records, unit inconsistencies and duplicates. Preparation includes documentation of sources, handling of missing data, outlier analysis based on physical reasoning, resampling and alignment of time series, and feature construction. Each step is recorded so that it can be reproduced, and the data processing is fitted using training data only to avoid leakage.

Where privacy or confidentiality applies, we recommend anonymisation or on-premises processing and agree on arrangements at scoping.

We consider interpretable models, tree-based methods, kernel methods, probabilistic models and neural networks, matched to data size and structure. For small datasets, regularised and physics-informed models often outperform deep networks. For images and signals with ample labelled data, convolutional and sequence models may be appropriate. Hyperparameter tuning uses nested validation to avoid optimistic estimates.

We compare against simple baselines and report the improvement with intervals.

Classical statistical models such as linear and generalised linear models, mixed models and survival analysis give interpretable parameters and confidence intervals, and they work well with modest data when the structure is known. Machine learning excels at flexible prediction from many variables when plenty of data exist. A sensible workflow starts with a simple statistical model, uses it as a baseline and moves to complex models only if they add measurable value on held-out data.

In many engineering studies, the most valuable output is understanding which variables matter and by how much, which favours interpretable models. We help choose and justify the approach.

Explanations from models, such as feature importance or partial dependence, are hypotheses to be checked against domain knowledge and, if possible, experiments. Physics-informed approaches embed known constraints. A model that predicts well but contradicts physics should be treated with suspicion and examined for leakage or confounding.

We work with the client's domain expertise to interpret results.

Deliverables typically include a data description, a documented pipeline, trained models with evaluation reports, figures and tables for publication, and a methods text suitable for a manuscript. We provide scripts and environment details to support reproducibility. We do not provide production deployment, monitoring services or guarantees of performance on future data.

If deployment is required, a separate engineering effort is needed, which we can discuss in general terms.

Examples of research applications include predictive maintenance studies, structural health monitoring analytics, materials property prediction, process optimisation, energy forecasting, surrogate models for expensive simulations, image-based inspection and quality prediction. These are examples of categories, not claims of past projects.

Each application has its own data issues, and we tailor the approach.

Describe your data, target and objective, and whether you want modelling support, review of an existing analysis or help writing. We will propose a plan and tell you what data or validation would strengthen the work.

Send a description of your data (type, size, collection method), the target you want to predict or understand, the performance that would be useful and any constraints on tools or privacy. We will respond with questions, a feasibility view and a proposed plan.