Admission Screener I Paid Subscription
Admission Screener is an AI-powered application designed to help academic institutions efficiently and consistently evaluate student admission applications. The solution reviews complete applicant dossiers—including personal statements, academic records, recommendation letters, resumes, and supporting materials—and classifies applications based on institution-defined criteria.
Using the institution’s evaluation rubric, Admission Screener assesses academic performance, written responses, extracurricular involvement, and recommendation strength. Applications are categorized as Exceptional, Average, Below Average, or Requires Further Review, with each classification supported by a clear, structured justification. This approach promotes transparency and enables auditable admissions workflows.
The Paid Subscription is ideal for individual admissions officers or small admissions teams that require full access to Admission Screener functionality. This plan supports up to 50 applicant screenings per term and includes:
Full rubric-based evaluation capabilities
Advanced application summaries
Accelerated processing for faster review cycles
Key Benefits
Reduces manual application screening time from weeks to hours
Supports consistent, rubric-aligned evaluations across departments
Flags incomplete or ambiguous applications for human review
Enables customizable criteria aligned to institutional priorities
Designed to integrate with existing admissions systems and portals
Built for Institutional Admissions
Admission Screener is designed to support high-volume admissions cycles while keeping admissions professionals in control of final decisions. By automating routine evaluation steps and highlighting cases that require closer attention, the solution allows teams to focus on strategic decision-making and holistic review.
The platform is well-suited for undergraduate, graduate, and scholarship admissions processes, helping institutions improve efficiency, consistency, and review transparency.
