No. Dirty data is not a blocker — it's our starting point. Our data engineering team's job is to clean, join, and normalize your data before Cue touches it. Data transformation is not an add-on. It is the product.

No. Our data scientists handle all of the technical complexity — ingestion, transformation, model training. Your team interacts with Cue in plain language. No SQL, no dashboards, no data team required on your side.

Four weeks to first insight. We spend the first three weeks ingesting, cleaning, and normalizing your data. By week four, Cue is ready to answer your key business questions. Predictive models are live by week six.

ChatGPT gives you statistically plausible answers. Cue gives you answers built from your actual enrollment and marketing data — cleaned, joined, and structured before it touches the model unlike a simple MCP connection that doesn't touch the data first. Generic AI doesn't know your institution. Cue does.

An AI analyst is software that answers business questions in plain language, using an organization's real data, instead of requiring someone to build a query, dashboard, or report by hand. Cue is an AI analyst built specifically for Education organizations — it works on a data layer that connects to your CRM, LMS, SIS, and marketing platforms, then answers enrollment and marketing questions the way a skilled human analyst would, complete with a recommendation and a next step.

A BI dashboard shows you pre-built charts that someone still has to configure and interpret. Cue answers the specific question you ask, in plain language, and tells you why something happened and what to do next — no dashboard building, no SQL, no waiting on a data team. Dashboards report what happened; Cue explains it and recommends action.

Cue is built for the full range of Education organizations — four-year institutions, community colleges, K-12 systems, and workforce or continuing education providers. Each deployment is trained on that organization's own enrollment terminology, program mix, and data sources, so the same platform adapts across sub-sectors without losing precision.

No. Cue is trained on your data, for your institution only. Your enrollment, marketing, and student data are never used to train models for other customers or shared across accounts.

Yes. Once your data is integrated and cleaned, our teams build predictive models for enrollment forecasting and retention risk, training Cue for precisely those questions. Predictive models are typically live within six weeks of onboarding.