Our Artificial Intelligence services

Predictive analytics

We take your historical records, whether that is sales transactions, sensor readings, or patient admissions, and train models that forecast future values. The output is a REST API endpoint your existing software can query in real time.

A typical project runs eight weeks. During weeks one and two we audit your data, identify gaps, and agree on the target metric. Weeks three through six cover feature engineering, model training, and validation. The final two weeks are for deployment, load testing, and documentation.

• Demand forecasting for retail and distribution
• Churn prediction for subscription businesses
• Equipment failure prediction for manufacturing
• Financial risk scoring

Natural language processing

Language models are only useful when they understand your vocabulary. A legal firm needs different entity recognition than a logistics company. We fine-tune open-source transformer models on your domain corpus so the system recognises the terms, abbreviations, and document structures that matter to your team.

Deliverables range from a standalone chatbot to a document classification pipeline that routes incoming emails or PDFs to the right department. We benchmark accuracy on a held-out test set drawn from your own data and share the results before deployment.

• Customer support chatbots with handoff to human agents
• Automated document tagging and routing
• Sentiment analysis on survey responses or social media
• Named entity extraction from contracts or medical records

Computer vision

We build image and video analysis systems for quality control, safety monitoring, and inventory management. Our process starts with an on-site visit to understand lighting conditions, camera angles, and the physical environment where the model will run.

Annotation is often the longest phase. We manage the labelling workflow, either in-house or with a vetted annotation partner, and run inter-annotator agreement checks to keep label quality above 95%. Models are deployed on edge devices when latency matters, or in the cloud when it does not.

• Defect detection on production lines
• Shelf-stock monitoring for retail chains
• Safety-gear compliance checks on construction sites
• Agricultural crop health assessment from drone imagery

AI readiness audit

Not sure whether your data is good enough to support a machine learning project? This two-week engagement answers that question. We review your databases, spreadsheets, and third-party feeds, then produce a report that scores data completeness, consistency, and volume against the requirements of the use case you have in mind.

The report includes a go/no-go recommendation, an estimated project timeline, a budget range, and a list of data-quality fixes you would need to make before model training can begin. Many clients use this report to build an internal business case for AI investment.

Ongoing model management

Production models degrade as the world changes. Customer behaviour shifts, product catalogues grow, and sensor calibrations drift. Our management retainer covers monthly performance reviews, automated retraining triggers, and priority bug fixes.

You get a shared Slack or Teams channel with a guaranteed four-hour response time during UK business hours. We also run quarterly strategy calls to discuss new use cases and whether existing models should be updated or replaced.