Machine Learning Development

Predictive analytics and forecasting

Predictive models turn your history into foresight: what demand will look like next month, which customers are likely to churn, which leads are worth chasing, what a property or claim is worth. We build predictive and forecasting models that give your team a reliable, quantified view of what is coming, so decisions are driven by evidence rather than instinct.

We frame each prediction around the decision it informs and the action it enables, then validate honestly so you know how much to trust it. The payoff is tighter planning, less waste and effort focused where it pays off.

• Demand and sales forecasting
• Churn and retention prediction
• Lead and customer scoring
• Price and value estimation
• Risk and default prediction
• Honest validation and confidence

Related: AI consulting & strategyBusiness intelligence

Recommendation systems

Recommendation engines lift revenue by showing the right thing to the right person at the right moment: products, content, offers or next best actions tailored to each user. We build recommenders that learn from behaviour and context and improve as they gather data, whether for an ecommerce catalogue, a content platform or an internal tool.

We tune recommenders to your real goal, order value, engagement or retention, and measure the uplift against a baseline so you know they are earning their place. We also handle the practical challenges, cold starts, sparse data and relevance, that decide whether a recommender actually works.

• Product and content recommendations
• Personalisation engines
• Next-best-action models
• Cold-start and sparse-data handling
• Relevance and ranking tuning
• Measured uplift against a baseline

Related: Generative AI development

Computer vision

Computer vision lets software see: inspecting products for defects, reading documents and forms, counting and tracking objects, verifying identity and classifying images at a scale and speed no human team could match. We build and train vision models on your imagery and deploy them where they cut manual effort and raise consistency.

From quality control on a production line to document extraction and visual search, we handle data labelling, model training and the deployment pipeline. Every build is validated on your real conditions, lighting, angles and edge cases, so it performs in the field, not just in the lab.

• Image classification and tagging
• Object detection and counting
• Defect and quality inspection
• Document and form extraction (OCR)
• Visual search
• Validation on real-world conditions

Natural language processing (NLP)

Most businesses sit on a mountain of text, emails, tickets, reviews, documents and chat, that no one has time to read. NLP turns that text into structured insight and action: classifying and routing messages, extracting key information, gauging sentiment, and summarising at scale. We build NLP models tuned to your language, domain and workflow.

Where a large language model is the better tool, we say so and build with it, and where a focused, lightweight model is faster and cheaper, we use that. The goal is the same: get the signal out of your text and into your decisions and systems.

• Text classification and routing
• Information and entity extraction
• Sentiment and intent analysis
• Summarisation at scale
• Topic and theme detection
• Domain-tuned language models

Related: Generative AI developmentAI agent development

Data preparation and feature engineering

Model quality is capped by data quality, which is why we spend real effort here. We assess, clean and shape your data, handle gaps and inconsistencies, and engineer the features that give a model something meaningful to learn from. This unglamorous work is usually what separates a model that performs from one that disappoints.

We also build the pipelines that keep data flowing reliably into the model in production, so the inputs stay as clean and consistent as they were in training. Good data engineering underneath is what makes the model trustworthy on day one and every day after.

• Data assessment and cleaning
• Handling gaps and inconsistencies
• Feature engineering and selection
• Training and test-set design
• Reliable production data pipelines
• Data quality monitoring

Related: Data engineeringCloud services

Model deployment, MLOps and monitoring

A model only creates value once it is in production and trusted. We deploy models as reliable services or batch pipelines, integrate them into your systems, and put proper MLOps around them: versioning, automated retraining, and monitoring for accuracy, drift and performance. This is how a model keeps earning its keep rather than quietly decaying.

Because the world changes, we watch for the moment a model's accuracy starts to slip and retrain before it becomes a problem. You get a model that is not just launched but maintained, with plain-English reporting on how it is performing.

• Model deployment as services or pipelines
• System and application integration
• Versioning and reproducibility
• Accuracy and drift monitoring
• Automated retraining
• Plain-English performance reporting

Related: DevOps & CI/CDCloud services