AI & Automation

It removes repetitive judgement work — reading documents, classifying requests, extracting data, forecasting demand — so staff spend their time on the decisions that genuinely need a person. We score candidate use cases on impact, data readiness, delivery effort and regulatory exposure before building anything, because most AI projects fail by starting with the technology instead of the bottleneck.

Less than most people assume for document and language tasks, and more than most assume for forecasting. Retrieval assistants and document extraction work on the documents you already have, with no training data required. Predictive models generally need at least a year of clean, consistent operational history before the output is trustworthy.

Yes, and any supplier who says otherwise is overselling. The engineering question is what happens when it does. We build evaluation harnesses to measure accuracy before launch, put human review in front of anything with real consequences, and monitor for drift after release so degradation is caught rather than discovered.

Not in the way we build it. We use commercial API tiers that contractually exclude your data from training, and for sensitive workloads we can keep processing entirely within your own infrastructure. Data residency and retention are decisions we make with you at design time, not defaults you inherit.

A focused proof of concept typically takes two to four weeks and exists to answer one question: does this work well enough on your real data to be worth funding properly. Full production delivery with monitoring and human review is usually a matter of months, depending on integration depth.