We tell you which AI is worth building, and build it if it holds up

A national telecom operator held years of network data and had nobody in-house to use it. We assessed its data and infrastructure, and that set the order of the work. The predictive network models we built came first, and they flag faults before they happen.

A hospital network wanted to know which patients were likely to come back. We tested the model against past patient outcomes before it went into use, then put its risk score inside the EHR clinicians already use, before discharge.

For a manufacturing group, demand and inventory forecasts go straight into the ERP, which is where the planners already work.

At Canam Consultants, an AI agent works inside the CRM on program look-ups, shortlists, document prep and follow-up emails. A person approves each transcript and recommendation letter before it goes anywhere. The agent now does 80% of the repetitive work, and each counselor can handle twice the caseload.

A metropolitan transit network's voice line answers in eleven languages. When its confidence drops below policy it passes the call to a person, and every call is logged for audit in the network's core systems. Seven calls in ten are resolved without one.

At a major financial institution we put machine-learning scoring inside its existing banking systems, so scoring and alerts run in real time.