How we deliver Artificial Intelligence projects

Discovery workshop

We run a half-day session with your team, usually on-site or over video. The goal is to identify one measurable business metric the AI should improve: cost per transaction, average handling time, defect rate, whatever matters most to you. We also audit the data you already collect. If there are gaps, we flag them now rather than three weeks into model training.

By the end of the workshop you receive a written brief that states the problem, the target metric, the data sources, and a rough timeline. No jargon, no 60-page slide decks.

Data preparation

Raw data is never ready. We spend one to three weeks cleaning, de-duplicating, and transforming your records into a format suitable for model training. This includes handling missing values, normalising date formats, and joining tables from different systems.

If you are working with sensitive data (patient records, financial transactions) we set up a secure processing environment that complies with UK GDPR requirements. Data never leaves your infrastructure unless you explicitly approve it.

Model development

Our engineers train several candidate models and compare them on a held-out test set. For tabular data we typically start with gradient-boosted trees (XGBoost or LightGBM) because they train fast and handle mixed feature types well. For text and document tasks we fine-tune transformer architectures.

You get a weekly progress report with accuracy, precision, recall, and F1 scores. We also run fairness checks to make sure the model does not discriminate on protected characteristics.

Integration and testing

The chosen model is wrapped in a containerised API (Docker, deployed on your preferred cloud or on-premise server). We write integration tests, load tests, and edge-case tests before connecting it to your live systems.

During this phase we also build the monitoring dashboard: response latency, prediction distribution, input data drift. If something shifts, the dashboard sends an alert to your Slack channel or email.

Pilot launch

We run the model in shadow mode alongside your existing process for two to four weeks. Predictions are logged but not acted upon yet. This lets us compare AI outputs against human decisions and catch issues before they affect real operations.

Once shadow-mode results meet the agreed accuracy threshold, we switch to live mode. Roll-out is gradual: 10% of traffic first, then 50%, then 100%, with a kill switch at every stage.

Ongoing support

Models degrade as the world changes. We offer a 12-month support package that includes quarterly retraining, drift monitoring, and bug fixes. If your data volume grows or a new use case emerges, we scope it as a separate mini-project with its own fixed price.