Services
Applied AI & Automation
Machine learning and automation aimed at a decision that changes — scoped against a baseline, measured honestly, and shipped into a real workflow.
Most automation projects fail on framing rather than modelling, so the first deliverable is a baseline and a definition of what improvement would look like. If a rules-based approach clears that bar, that is what gets recommended — a simpler system that works is a better outcome than a model that impresses.
Work spans computer vision for inspection imagery, document and workflow automation, forecasting and anomaly detection on operational data, and retrieval-augmented assistants over internal knowledge. Evaluation uses held-out data and reports the failure modes, not only the headline metric.
How the work runs
Define the decision and the baseline → data review and feasibility → prototype → evaluation on held-out data with failure modes documented → integration → handover with a monitoring plan.