How our Artificial Intelligence process works

Initial conversation

We start with a 45-minute call. You describe the business problem, and we ask about the data you have, the systems it lives in, and what a successful outcome looks like. There is no charge for this call. By the end, both sides know whether there is enough substance to justify a discovery sprint. If there is not, we will tell you directly rather than drag things out.

Discovery sprint

Two of our engineers spend two weeks with your data. They profile it, check for gaps, assess label quality and test a simple baseline model. At the end of the sprint, you receive a written report that covers: whether the problem is solvable with the data you have, what accuracy range is realistic, which model architecture we recommend, a fixed-price quote for the build phase, and an estimated timeline. The sprint costs £12,000. If the data is not ready, we list exactly what needs to change before a model would be viable.

Data engineering and feature design

Good models depend on good features. In this phase, we build the data pipeline that cleans, transforms and joins your raw data into the feature set the model will train on. We write automated quality checks that flag missing values, schema changes and distribution shifts. If your data lives in multiple systems, we set up the connectors during this phase so the pipeline runs end to end without manual exports. You review the feature definitions in a shared document, and we adjust based on your domain knowledge.

Model training and validation

We train candidate models, compare them on the metrics we agreed during discovery, and run them against a held-out test set that neither we nor the model have seen before. For regulated use cases, we also produce a bias audit and an explainability report at this stage. You get a fortnightly demo where we show the model running on real data and walk through the performance numbers. If accuracy is below the agreed threshold, we iterate on features or architecture before moving to deployment.

This phase overlaps slightly with data engineering because we often discover that adding or restructuring a feature improves performance enough to justify the extra pipeline work.

Deployment and integration

The model goes live. We package it as a REST API, a batch job, or an embedded module depending on how your systems need to consume predictions. We deploy to your infrastructure or to a private cloud tenant we manage. Load testing, failover configuration and access controls are all part of this phase. We also build a monitoring dashboard that tracks prediction volume, latency, accuracy (if ground-truth labels are available) and data drift. Your team gets a short training session on reading the dashboard and escalating alerts.

Monitoring and retraining

Models degrade as the world changes. Customer behaviour shifts, product catalogues grow, regulations evolve. During the first 90 days after launch, we monitor performance and retrain at no extra charge if accuracy drops below the agreed threshold. After that window, you can either manage retraining in-house using the pipeline we built, or sign a support contract where we handle it. Support contracts are monthly, with no lock-in beyond 30 days.