Our Artificial Intelligence services
Each engagement starts with your data and your business question. We pick the simplest model architecture that answers it reliably, then harden it for production.
Predictive analytics
We take your historical records, whether that is five years of sales transactions or six months of sensor readings, clean them, engineer features and train models that forecast what happens next. The output is a versioned model behind a documented API your developers can call from any language.
- Demand forecasting for retail and supply-chain teams
- Churn prediction with probability scores per customer
- Anomaly detection on time-series data (fraud, equipment failure)
- Automated retraining pipelines triggered by accuracy drift
Typical project length: six to ten weeks from kick-off to production deployment. We provide a monitoring dashboard so you can see prediction accuracy, latency and throughput at a glance.
Natural language processing
Text is messy. Abbreviations, typos, domain jargon and multiple languages all appear in real-world corpora. We fine-tune large language models on your annotated examples so they understand the vocabulary your staff and customers actually use.
- Intent classification for chatbots and voice assistants
- Named-entity recognition in legal, medical or financial documents
- Sentiment analysis across product reviews and social channels
- Summarisation of long reports into structured briefs
We benchmark every model against a held-out test set and report precision, recall and F1 before handover. If the numbers fall short of the agreed threshold, we iterate until they don't.
Computer vision
Cameras generate enormous amounts of data. Our job is to make that data useful: counting objects, detecting defects, reading labels, tracking movement. We design convolutional and vision-transformer architectures sized to your hardware constraints.
- Defect detection on production lines (sub-100ms inference)
- Object counting and tracking from CCTV or drone footage
- OCR pipelines for handwritten forms and legacy documents
- Edge deployment on NVIDIA Jetson, Coral or Raspberry Pi
For a recent food-manufacturing client, our defect classifier achieved 97.3% recall on under-filled packages, catching items that visual inspection by staff had missed roughly 8% of the time.
MLOps and model lifecycle management
A model that sits in a notebook is a prototype. A model behind a CI/CD pipeline with version control, automated tests and rollback capability is a product. We build the second kind.
- Containerised model serving with Docker and Kubernetes
- Feature stores for consistent training and serving data
- A/B testing infrastructure for safe model rollouts
- Drift detection alerts piped to Slack, Teams or email
If you already have data scientists producing models but struggle to get them into production, this service closes the gap. We can also train your team to manage the pipeline after handover.
How a typical engagement works
Discovery
We audit your data sources, define success metrics and agree on scope. This takes one to two weeks.
Prototyping
We build a minimal model, share results and iterate on feature engineering with your domain experts.
Production
The validated model is containerised, tested and deployed behind an API with monitoring in place.
Support
Ninety days of included support. We monitor accuracy, retrain if needed and document everything.
Ready to start?
Tell us about your data and your business question. We will reply within one working day with an honest assessment of whether AI is the right tool and, if so, a rough scope and timeline.
Get in touch