AI & Data
Analytics

Not necessarily. The amount of data you need depends heavily on the problem. Some use cases (anomaly detection, simple classification) work well with thousands of records. Others need millions. We'll assess your data in the discovery phase and tell you honestly whether it's sufficient or what data collection plan you'd need.

Yes. This is one of the most common engagements we take on — embedding AI capabilities like semantic search, document summarisation, recommendation engines, or natural language interfaces into an existing platform via APIs or SDKs. We handle the AI infrastructure and expose clean interfaces for your existing development team to consume.

We implement drift detection pipelines that monitor your model's input distributions and prediction quality over time. When drift is detected, automated or human-triggered retraining kicks in. We also track model performance against business KPIs in your dashboards — not just technical ML metrics.

All data handling is designed with privacy-by-default. We implement anonymisation and pseudonymisation where required, ensure GDPR-compliant data processing agreements are in place, and avoid sending sensitive personal data to third-party LLM APIs without appropriate controls. Regulated industries (healthcare, finance) are a common speciality for us.