Services in detail
Click through to the contact form on our home page to discuss any of these. We typically respond within one working day.
Predictive analytics and forecasting
We build regression, classification and time-series models that answer concrete business questions: how many units will we sell next month, which customers are likely to churn in the next 90 days, what price maximises margin without losing volume.
The process starts with your historical data. We clean it, engineer features and test multiple algorithms (gradient-boosted trees, linear models, sometimes neural nets) against a held-out validation set. You see the accuracy metrics before we deploy anything.
- Demand and sales forecasting (daily, weekly or monthly granularity)
- Customer churn prediction with ranked risk scores
- Dynamic pricing models trained on competitor and elasticity data
- Anomaly detection for fraud, equipment failure or quality control
Typical timeline: four to eight weeks from kickoff to a production-ready API. Retraining pipelines run automatically on a schedule you choose.
Natural language processing and document intelligence
Unstructured text is one of the richest data sources most companies ignore. Emails, contracts, support tickets, medical letters: they contain information that never reaches a spreadsheet. Our NLP pipelines extract that information and turn it into structured, queryable data.
We use a combination of OCR (for scanned documents), named-entity recognition, text classification and extractive question-answering. The models run on your infrastructure and process a typical A4 page in under four seconds.
- Invoice and receipt data extraction (supplier, amounts, line items, dates)
- Contract clause identification and risk flagging
- Support-ticket categorisation and priority scoring
- Sentiment analysis on customer feedback or reviews
When the model is uncertain about an extraction, it flags the field for human review rather than guessing. This keeps accuracy above 95% on most document types after a two-week fine-tuning period on your real documents.
Conversational AI and chatbots
We design and deploy chatbots grounded in your own knowledge base. Unlike generic chatbot builders, our systems cite the source document for every answer. Your support team can verify responses in seconds, and your customers get accurate, specific information instead of vague suggestions.
The bot connects to your existing channels: website widget, Microsoft Teams, Slack or a custom API endpoint. We handle the retrieval-augmented generation (RAG) architecture, embedding your documents into a vector store that the language model queries at inference time.
- Customer-facing FAQ bots with source citations
- Internal knowledge assistants for HR policies, technical documentation or onboarding
- Voice-enabled assistants for phone-based customer service
We monitor hallucination rates weekly for the first three months. If the bot produces an unsupported answer, we trace it back to the retrieval step and fix the root cause, whether that is a missing document, a chunking problem or a prompt issue.
Computer vision
Our computer-vision service covers object detection, image classification and visual inspection. A food-packaging client uses our system to spot label defects on a conveyor belt at 120 items per minute. A property firm uses it to tag and sort thousands of listing photos by room type automatically.
We fine-tune pre-trained models (YOLO, EfficientNet or similar) on your labelled images. If you do not have labelled data yet, we help you set up an annotation workflow with open-source tools like Label Studio and guide your team through the first batch.
- Quality-control inspection on production lines
- Automatic image tagging and categorisation
- Object counting and measurement from drone or CCTV footage
MLOps and model lifecycle management
A model that works in a Jupyter notebook is not a product. We build the infrastructure that turns it into one: containerised serving, automated retraining, drift monitoring and alerting. If your existing models are stuck in notebooks or running on a single engineer's laptop, this service gets them into a reliable, repeatable pipeline.
- Model packaging with Docker and deployment to Kubernetes or cloud-managed endpoints
- CI/CD pipelines that retrain and validate models on new data before promoting to production
- Data and concept drift dashboards with configurable alert thresholds
- Cost optimisation for GPU-based inference (right-sizing instances, batching requests)
We work with AWS, Azure and GCP. If you have a strong preference or existing cloud contracts, we build within that ecosystem. On-prem deployments are also possible for clients with data-sovereignty requirements.
How every engagement works
The same five steps apply whether the project takes four weeks or four months.
Discovery
We spend two weeks reviewing your data, defining success metrics and estimating feasibility. You get a written report with a go/no-go recommendation.
Prototype
A working model on a sample of your data, tested against the metrics we agreed on. This usually takes two to three weeks.
Production build
We containerise the model, write API endpoints, set up logging and connect it to your systems. Duration varies by integration complexity.
Validation
One to two weeks of parallel running: the model operates alongside your existing process so we can compare outputs and catch edge cases.
Support
Three months of included post-launch support: monitoring dashboards, retraining runs and on-call troubleshooting during UK business hours.
Pricing approach
We quote a fixed price per project, not hourly rates. The discovery phase has its own fee (typically £2,000 to £4,000 depending on data complexity). If discovery concludes that the project is not feasible, you owe nothing beyond the discovery fee. No lock-in, no retainers.
Full project costs vary widely. A straightforward churn-prediction model on clean tabular data might cost £8,000 to £15,000. A multi-language document-extraction pipeline with custom OCR fine-tuning could be £25,000 to £45,000. We will give you a detailed breakdown after discovery, and you approve each milestone before we bill for it.
Ready to find out what your project would cost? Get in touch and describe what you are trying to achieve. We will schedule a short call to discuss scope and next steps.