Retail
Demand forecasting, recommendation engines, price optimisation
Predictive analytics
Turn historical data into forward-looking decisions
We build regression and classification models that answer questions like "how many units will we sell next quarter?" or "which customers are most likely to cancel?" The models run on your own infrastructure or on a managed cloud instance we set up for you.
A typical project begins with your last 18 to 36 months of transactional data. We clean it, engineer features and test several algorithms: gradient-boosted trees, linear models with regularisation, and occasionally a small neural network if the data warrants it. The best performer gets deployed behind a REST API your existing software can call.
- Demand forecasting for inventory planning
- Customer churn prediction with actionable risk scores
- Revenue projection broken down by product line or region
- Lead scoring for sales teams
Delivery time: four to eight weeks from data handover to production API. You receive full model documentation, a Jupyter notebook showing the training process, and a 90-minute walkthrough with your analysts.
Natural-language processing
Extract meaning from text at scale
Contracts, support tickets, survey responses, clinical notes: your organisation produces thousands of documents every month. Reading them manually is slow. Misreading them is expensive.
We fine-tune transformer-based language models on your specific vocabulary. A legal firm we worked with had 14 years of contract PDFs. We trained a named-entity recognition model that pulls out party names, obligation clauses and termination dates with 94% accuracy, saving paralegals roughly 20 hours per week.
- Chatbot development with domain-specific knowledge bases
- Sentiment analysis on customer reviews or social-media mentions
- Document classification and routing
- Information extraction from unstructured text into structured databases
We handle data privacy carefully. Training can run entirely on-premise if your compliance team requires it. Models are versioned and reproducible so you can audit exactly which data was used in each training run.
Computer vision
Teach machines to see what matters
Our computer-vision team has deployed inspection systems in food manufacturing, automotive parts assembly and document digitisation. The common thread: a camera captures an image, our model classifies or segments it in under 200 milliseconds, and the result feeds into your workflow automatically.
For a baked-goods producer in Bradford, we trained an object-detection model on 12,000 annotated images of bread rolls. The model identifies misshapen or burnt products on the conveyor belt and triggers a pneumatic arm to reject them. Rejection accuracy sits at 96.2%, up from 81% with the previous rule-based system.
- Defect detection on production lines
- Optical character recognition for invoices, receipts and forms
- Object counting and tracking in warehouse environments
- Medical image analysis (X-ray, MRI) for NHS and private clinics
We annotate training data in-house using a team of two dedicated annotators. If you already have labelled data, the project moves faster and costs less.
AI strategy and MLOps
Plan before you build, and keep it running after
Not every company needs a custom model. Sometimes the right answer is a well-configured off-the-shelf tool. Our strategy service exists to figure that out before you spend five figures on development.
During a Discovery day we map your data landscape, interview the people who will use the AI outputs, and identify where automation will save the most time or money. You get a written brief ranking opportunities by expected ROI, technical feasibility and data readiness. Roughly 30% of the time, we recommend a commercial product rather than a custom build, and we tell you which one.
Once a model is in production, MLOps keeps it healthy. We set up CI/CD pipelines for model retraining, automated drift detection, and alerting via Slack or email. Our monitoring dashboard tracks accuracy, latency, input-data distributions and resource usage. When a metric crosses a threshold you have defined, the system retrains automatically or pages an engineer.
- AI readiness assessment and roadmap
- Vendor evaluation for off-the-shelf tools
- CI/CD pipeline setup for model training and deployment
- Drift detection and automated retraining
- Cost optimisation for cloud GPU spend
Industries we work with
Our models run in production across these sectors. Each industry has its own data quirks and compliance requirements, and we have learned them through direct experience rather than textbooks.
Financial services
Fraud detection, credit scoring, regulatory document parsing
Healthcare
Clinical NLP, radiology image triage, patient-flow prediction
Manufacturing
Visual inspection, predictive maintenance, yield optimisation
Logistics
Route optimisation, warehouse automation, shipment ETA prediction
Legal
Contract analysis, due-diligence automation, case-law search
How we deliver every project
Regardless of which service you choose, the delivery process follows the same disciplined structure. Deadlines are real, and scope is fixed at each gate.
Scope and sign-off
We agree on the success metric, the data sources, the timeline and the budget. Nothing starts until both sides have signed the statement of work.
Data preparation
Our data engineers clean, join and validate your datasets. We document every transformation so the pipeline is reproducible.
Model development
We train, evaluate and iterate. You see progress weekly through a shared dashboard and a short written update every Friday.
Testing and integration
The model is containerised, tested against edge cases, and integrated with your systems via API or batch pipeline.
Handover and support
Documentation, training session, and a 60-day warranty. If you sign a Maintain contract, monitoring begins immediately.