Latte provides an AI‑driven operator that automates data‑heavy workflows for higher‑education teams such as advancement, enrollment, and student success. The platform automates tasks like importing student rosters, syncing alumni mentorship data between Blackbaud CRM and Banner, and generating event invitations while keeping human approval steps built in. By handling data entry, outreach, reporting and event operations, Latte lets staff focus on direct student engagement.
Funding
Funding not disclosed
Founders
Product
Problem
Higher‑education administrative teams must manually move data between systems such as Blackbaud CRM and Banner to run mentorship programs, send event invitations, track RSVPs, and generate reports. This manual effort consumes staff time and creates opportunities for errors, limiting the ability to focus on student engagement.
Solution
Latte provides an AI‑driven operator that automates repetitive data‑handling tasks while preserving human oversight. The platform connects directly to existing higher‑ed systems, extracting, transforming, and loading data needed for mentorship coordination, event outreach, enrollment nudges, and retention interventions. Workflow steps are executed automatically, and results are presented for staff approval before final actions are taken. By centralizing logic in the AI operator, teams can reduce manual entry, improve data consistency, and allocate more time to direct student interaction. The solution also offers predictive insights, such as applicant melt risk, to guide proactive outreach.
Target Audience
Primary customers are administrative teams in higher‑education institutions, including advancement/alumni relations, enrollment and admissions, and student success or retention offices.
Features
- Native integrations with Blackbaud CRM and Banner for bidirectional data sync
- Automated orchestration of mentorship program recruitment, event invitation distribution, and RSVP reconciliation
- AI‑generated reports and analytics for donor briefings, enrollment yield, and retention risk detection
- Human‑in‑the‑loop approval screens that let staff review and confirm automated actions
- Predictive modeling to identify at‑risk applicants or students and suggest targeted interventions
- No separate system of record; the operator works on top of existing campus platforms