Our Artificial Intelligence services
Every service below follows the same pattern: we audit your data first, agree a fixed price, build the model, deploy it, and monitor it. Here is what each service includes.
Predictive analytics
We train time-series and regression models on your historical data to forecast demand, revenue, churn, or any other metric you track over time. The models run as a REST API that your existing software calls on a schedule you choose: hourly, daily, or weekly.
A typical engagement starts with 18 to 36 months of historical records. We clean the data, engineer features (lag variables, rolling averages, calendar effects), and test multiple model architectures. The final model is the one that scores best on a hold-out test set drawn from your most recent data, not from a random split.
- Data audit and feature engineering report
- Trained model deployed behind a versioned API
- Monitoring dashboard with accuracy drift alerts
- Two scheduled retraining cycles within the 90-day support window
Natural language processing
Text data is everywhere: customer reviews, support tickets, contracts, internal reports. We build models that read this text and extract structured information from it. Common tasks include sentiment classification, named-entity recognition, topic clustering, and document summarisation.
We fine-tune open-source transformer models (typically in the 350M to 7B parameter range) on your labelled examples. If you do not have labelled data, we can set up an annotation workflow with a tool like Label Studio and guide your team through the labelling process before training begins.
All models run within your infrastructure. We do not route your data through external APIs, which means you keep full control over sensitive text.
- Annotation guidelines and quality-control workflow
- Fine-tuned transformer model packaged as a Docker container
- Batch and real-time inference endpoints
- Evaluation report with precision, recall, and F1 per class
Computer vision
Our computer vision work covers two main areas: defect detection on production lines and object counting from images or video feeds. We have deployed inspection systems in food manufacturing, precision engineering, and pharmaceutical packaging.
The typical workflow involves collecting 2,000 to 10,000 labelled images, training a convolutional or vision-transformer model, and deploying it to an edge device (usually an NVIDIA Jetson or an industrial PC) so inference happens on-site with sub-100ms latency. No images leave your facility.
- Image collection and labelling support
- Trained model optimised for edge hardware
- Integration with your PLC or MES system
- False-positive and false-negative rate report
Data strategy and readiness
Some clients come to us before they have enough data for a model. In those cases, we run a data-strategy engagement: we map out every data source you currently have, identify gaps, and design a collection plan that will make future AI projects feasible.
This is a consulting engagement, not a software build. The deliverable is a written report, typically 20 to 40 pages, with specific recommendations. We cover data governance, storage architecture, and the minimum viable dataset for each AI use case you want to pursue.
We charge a flat fee for this work, usually between £3,000 and £6,000 depending on the number of data sources we need to audit. Clients who proceed to a model build within 12 months receive a credit equal to 50% of the strategy fee against the build cost.
- Data source inventory and quality scorecard
- Gap analysis with collection plan
- Storage and governance recommendations
- Prioritised list of AI use cases ranked by feasibility and ROI
MLOps and model monitoring
A model that works on launch day can degrade within weeks if the incoming data shifts. We build monitoring pipelines that track prediction accuracy, data distribution, and latency in real time. When a metric crosses your threshold, the system triggers an alert and, optionally, kicks off an automated retraining job.
We use open-source tools (MLflow, Evidently, Grafana) so you are never locked into a proprietary platform. The entire pipeline runs in your cloud account or on-premise servers.
- CI/CD pipeline for model versioning and deployment
- Data-drift and concept-drift monitoring
- Automated retraining with human-in-the-loop approval
- Grafana dashboards for latency, throughput, and accuracy
Pricing overview
We quote fixed prices after the discovery phase, so the figures below are ranges based on past projects. Your actual quote depends on data complexity, model type, and deployment requirements.
- Data quality audit
- Feasibility assessment
- Success metric definition
- Written report with recommendation
- Feature engineering and training
- API deployment
- Integration with your systems
- 90-day post-launch support included
- Model monitoring and drift alerts
- Scheduled retraining on new data
- Priority bug fixes (4-hour SLA)
- Quarterly performance review call
How a project moves from enquiry to production
Every engagement follows these five stages. We do not skip steps, because rushing past data validation is the most common reason AI projects fail.
Enquiry and scoping
You describe the problem. We ask questions about your data, your systems, and the outcome you want. This stage is free and usually takes one to three emails plus a 45-minute call.
Discovery phase
We audit your data, test basic models to confirm feasibility, and define the metrics that will determine success. You receive a written report at the end.
Model development
We train, validate, and iterate on the model. You see weekly progress updates with metric snapshots. We do not disappear for two months and reappear with a finished product.
Deployment
The model goes into your production environment. We handle containerisation, API setup, and integration with your existing software. We run parallel testing before switching over.
Monitoring and support
For 90 days after launch we monitor accuracy, retrain twice on fresh data, and fix any integration bugs. After that, you can continue on a monthly retainer or manage the model yourself.
Have a project in mind?
Tell us about the problem you want to solve and the data you have available. We will get back to you within one working day with an honest assessment.
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