Our service areas
We focus on five areas where machine learning delivers clear, measurable value for mid-sized businesses.
Predictive analytics
We build models that answer a specific business question: which customers are likely to churn next quarter, how many units of product X will you sell in week 12, or which machines on the factory floor are most likely to fail in the next 30 days.
The typical engagement uses your existing transactional data. We clean it, engineer features, and test several algorithms (gradient-boosted trees, logistic regression, sometimes a small neural network) to find the best balance of accuracy and interpretability. You receive a trained model deployed as a REST API or integrated directly into your database via a scheduled job.
- Data audit and feasibility report
- Feature engineering and model training
- API or batch-pipeline deployment
- Documentation and a recorded walkthrough
Natural-language processing
Text data is everywhere: support tickets, survey responses, contract clauses, social-media mentions. We turn that unstructured text into structured signals your team can act on.
Common deliverables include sentiment classifiers for customer feedback, topic-modelling dashboards that surface recurring complaints, and entity-extraction pipelines that pull key dates, amounts, and names from legal documents. We fine-tune transformer-based language models on your domain-specific vocabulary so accuracy is far higher than a generic off-the-shelf tool.
- Corpus preparation and annotation guidance
- Model fine-tuning on your domain text
- Dashboard or API integration
- Retraining schedule and drift alerts
Computer vision
If your process involves looking at images or video and making a judgement, there is a good chance a convolutional neural network can do it faster. We have built defect-detection systems for a packaging manufacturer (catching torn seals on a production line running at 120 units per minute) and a crop-health monitor for an agricultural co-op that classifies drone imagery into healthy, stressed, and diseased zones.
We handle labelling strategy, model architecture selection, training, and edge deployment if low latency is required.
- Image/video data assessment
- Labelling workflow setup
- Model training and validation
- Cloud or edge deployment
Data pipeline engineering
A model is only as good as the data feeding it. We design and build the extraction, transformation, and loading pipelines that keep your models supplied with clean, timely data. This includes scheduled ingestion from APIs, databases, flat files, and cloud storage, plus validation checks that alert your team when data quality drops below a threshold.
We work primarily with Python, Apache Airflow, and cloud-native services on AWS or Azure, but we adapt to whatever stack you already run.
- Source mapping and schema design
- ETL/ELT pipeline build
- Data-quality monitoring and alerting
- Infrastructure-as-code handover
AI strategy and audit
Not sure where AI fits in your business? We run a two-day on-site audit. Day one: we interview stakeholders across departments, review your data assets, and map existing workflows. Day two: we present a prioritised list of opportunities ranked by estimated impact and feasibility, along with rough budgets and timelines.
There is no obligation to hire us for the build. Some clients take the audit report to their internal engineering team. Others use it to secure budget approval from their board before engaging us for implementation.
- Stakeholder interviews (up to 8 people)
- Data-asset inventory
- Opportunity scoring matrix
- Executive-ready report (PDF, max 20 pages)
Indicative pricing
Every project is scoped individually, but these ranges give you a realistic starting point.
Strategy audit
Two-day on-site assessment plus a written report. Travel within Wales included; England and Scotland add travel costs at actuals.
Model build
Covers data prep, training, deployment, documentation, and 60 days of post-launch support. Price depends on data complexity and integration requirements.
Ongoing retainer
Quarterly model retraining, drift monitoring, and up to 8 hours of ad-hoc support per month. Minimum three-month commitment.
Ready to talk specifics?
Send us a brief description of what you want to achieve. We reply within one working day with an honest take on whether AI is the right tool and a rough budget range.
Get in touch