Our Artificial Intelligence services in detail

Five service areas, each with a clear scope, deliverable and price range. Pick what fits or let us design a custom engagement.

Service areas

Predictive analytics

We build regression and classification models that forecast outcomes from your historical data. Demand planning, churn prediction, credit scoring, equipment failure alerts: if the pattern is in the numbers, we can find it and put it behind a REST API your developers can call from any system.

A typical engagement starts with a one-week data audit where we assess quality, volume and feature relevance. Training and evaluation take two to four weeks. Deployment, including CI/CD pipeline setup and monitoring dashboards, adds another week. Total timeline: four to six weeks for most tabular-data problems.

  • Data audit and feature-engineering report
  • Trained model with documented performance metrics
  • REST API endpoint deployed on your cloud
  • Monitoring dashboard for accuracy and data drift
  • 90-day post-launch support included

Natural-language processing

Text is messy. Contracts, support tickets, survey responses, social-media mentions: they all contain useful signals buried under inconsistent formatting and domain jargon. We fine-tune transformer-based models on your specific vocabulary so the system classifies, extracts or summarises with the accuracy your team needs.

Projects in this area require labelled examples. If you do not have them, we can set up a labelling workflow using your subject-matter experts and manage the process. Expect six to ten weeks from kick-off to a production-grade NLP pipeline.

  • Labelling workflow setup (if needed)
  • Fine-tuned language model on your domain
  • Integration with your ticketing, CRM or document system
  • Accuracy benchmarks against a human baseline
  • Retraining schedule and data-pipeline automation

Computer vision

We train object-detection, segmentation and classification models for visual inspection, counting, and anomaly detection. Our past projects include defect detection on a bottling line (catching 98.3% of defects at 12 frames per second), parcel sorting in a distribution centre, and preliminary screening of dermatology images for a telehealth platform.

Computer-vision projects often need edge deployment: the model runs on a device next to the camera rather than in the cloud. We handle the full stack, from labelling and augmentation through to TensorRT or ONNX optimisation for NVIDIA Jetson or similar hardware.

  • Image labelling and augmentation pipeline
  • Trained detection or classification model
  • Edge or cloud deployment with latency targets
  • Real-time alerting integration
  • Performance monitoring and scheduled retraining

Data strategy and readiness

Not every company is ready for a machine-learning project. Sometimes the data is scattered across spreadsheets, the labelling is inconsistent, or nobody has defined what "success" looks like. We run a two-week assessment that answers three questions: what data do you have, what shape is it in, and which AI use case will deliver the highest ROI given your current state?

The output is a written report with a prioritised roadmap, estimated costs for each use case, and a list of data-quality issues that need fixing before any model training begins. Many clients use this report to secure internal budget approval.

  • Stakeholder interviews (up to six sessions)
  • Data inventory and quality scoring
  • Use-case prioritisation matrix
  • Roadmap with timelines and cost estimates

Ongoing support and monitoring

Models degrade. The data your customers generate in January looks different from what they generate in July. We offer monthly support plans that include automated drift detection, scheduled retraining, priority bug fixes and a dedicated Slack channel for your engineering team.

You can add this plan to any project we deliver, or bring us models built by another team. We will audit the existing system, set up monitoring, and take over maintenance so your internal engineers can focus on product work.

  • Automated accuracy and drift alerts
  • Monthly retraining runs
  • Priority bug-fix SLA (response within four hours)
  • Quarterly performance review call

Indicative pricing

All prices are fixed quotes, not day rates. The figures below are starting points; the actual price depends on data complexity and integration requirements. The scoping call is free.

Proof of concept

£8,000
one-off, 4 weeks
  • Single model, single endpoint
  • Data audit included
  • Performance report
  • 30-day monitoring
Get started

Production system

From £25,000
fixed quote, 6–12 weeks
  • Full pipeline: data to deployment
  • CI/CD and retraining automation
  • Monitoring dashboards
  • 90-day post-launch support
  • Documentation and knowledge transfer
Get started

Monthly support

£1,800/mo
rolling, cancel any time
  • Drift monitoring and alerts
  • Monthly retraining
  • 4-hour bug-fix SLA
  • Quarterly review call
Get started

How a project works

Every engagement follows the same five-step process. We have refined it over 47 production deployments.

Scoping call

A 45-minute video call where we discuss your problem, your data and your definition of success. Free, no commitment.

Data audit

We examine your data for quality, volume and feature relevance. You receive a written summary with recommendations.

Model development

Training, evaluation and iteration. We share results on fortnightly calls so you can steer the work.

Deployment

The model goes live on your infrastructure with automated tests, logging and a monitoring dashboard.

Monitoring

We track accuracy and drift for 90 days. If performance drops below the agreed threshold, we retrain at no extra cost.

Ready to talk?

Call us on +44 7586 802017 or email [email protected]. You can also fill in the contact form and we will reply within one working day.

659 Cavendish Road, St. Torp, Scotland, XF2 7CC, United Kingdom