Scholarly provides an AI-driven platform for higher education institutions to centralize faculty data and streamline administrative processes. The system automates faculty evaluations, eliminates manual data entry, and generates workload analytics tailored for diverse academic units. This solution enhances faculty productivity and institutional reporting accuracy through specialized, adaptable workflows.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Higher education institutions face challenges in efficiently managing faculty data, tracking workloads, and conducting evaluations due to reliance on manual processes and disparate systems. This often leads to data silos, increased administrative burden, and limited visibility into faculty contributions and performance.
Solution
Scholarly provides a centralized platform designed to streamline faculty data management, automate workload tracking, and improve evaluation processes within higher education institutions. The platform integrates with existing systems to eliminate manual data entry and offers real-time dashboards for enhanced transparency and efficiency. By centralizing faculty information and automating key workflows, Scholarly enables institutions to gain insights into faculty productivity, allocate resources effectively, and simplify accreditation reporting. The system adapts to the specific needs of various academic departments, including medical, art & science, business, law, and engineering schools.
Target Audience
The primary target audience includes higher education institutions, specifically deans, faculty affairs administrators, and institutional research departments responsible for managing faculty data, workload, and evaluations.
Features
- Centralized faculty data repository for managing profiles, accomplishments, and activities
- Automated workload tracking across different dimensions such as rank, gender identity, and race/ethnicity
- AI-driven tools to eliminate manual data entry and streamline data collection
- Real-time dashboards providing insights into faculty productivity and performance
- Customizable evaluation workflows to adapt to institutional processes
- Integration with existing systems to ensure seamless data flow
- Support for diverse faculty profiles, including those in academic medicine, arts, and sciences
- Automated accreditation reporting to simplify compliance