Artificial Intelligence

A traditional model returns a prediction — a score, a class, a forecast — and a person decides what to do with it. An agentic system plans a sequence of steps, calls tools and APIs, and carries a task through to completion, escalating when confidence drops. The engineering differs accordingly: agents need memory, tool contracts, loop and budget controls, and an audit trail for every action taken.

Not always, but data readiness decides which use cases are viable. Language and vision work can start from pretrained models applied to your existing documents or images, while predictive work needs labelled historical records with enough volume and consistency to learn from. Our first step is an assessment that ranks candidate use cases by data quality, business value and review burden.

You do. Trained weights, training and evaluation code, prompts, pipelines and infrastructure definitions are delivered into your repositories and cloud accounts. We build and hand over rather than hosting the system on your behalf, and handover includes retraining runbooks and monitoring dashboards. An optional support agreement is available if you want us involved afterwards, but it is not a condition of the build.

Models drift as the underlying data changes, so the monitoring ships with them. That means baseline metrics recorded at launch, drift and data-quality checks running on a schedule, alerting when scores fall below an agreed threshold, and retraining pipelines your team can trigger. Because the pipelines and evaluation sets are handed over with the code, your engineers can retrain without depending on us.