Scholarly
AI NativeAI-native faculty lifecycle software for higher education.
Est 2023|Valuation $14M|Raised $5M
MarketsPerformance Management
AI-native faculty lifecycle software for higher education.
Published Sep 21, 2026
The vendor does not appear to have a wide footprint on major B2B review platforms under the exact product name in the materials reviewed. Search results frequently return similarly named products, complicating independent evidence collection. As a result, available customer feedback is disproportionately vendor-published or institution-authored documents rather than a broad set of independent platform reviews.
Vendor-published case studies describe features such as AI-based expertise tagging, natural-language search, and consolidated faculty profiles. These materials report outcomes such as improved discoverability and reduced duplicate data entry; however, where quantitative metrics are presented they are often vendor-supplied and not independently verified in the public materials.
Direct community discussion about this vendor was limited in the materials reviewed. Broader community discussions about adjacent or competitor tools illustrate common user concerns in this product category—billing transparency, workflow friction, and support responsiveness—that buyers typically consider when evaluating faculty workflow software.
Reported selection drivers in vendor and institution-authored materials include:
Primary buyers/users: universities and colleges, including faculty affairs/provost offices, deans’ offices, department chairs, and academic HR/administration. The vendor markets tailored propositions for different school types (e.g., medical schools, law schools, engineering).
Positioning: Scholarly is presented as a newer platform designed specifically for faculty information and activity reporting, positioned as an alternative to legacy faculty information systems.
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Download full report →In these scenarios, institutions may consider piloting the vendor alongside incumbents such as Interfolio and Watermark.
Publicly observable limitations
Roadmap signals
Differentiators (vendor-claimed)
Areas where competitors may have advantages
An institution-authored report describing a small prototype deployment with a limited cohort of professors characterizes Scholarly’s typical use cases (centralizing faculty data, managing promotion and tenure processes, tracking faculty activity reporting). That report notes the prototype’s emphasis on making faculty expertise searchable and on translating academic terminology to industry-relevant language. Some comparison elements in the report are explicitly noted as provided by the vendor, which reduces independence for those specific claims.
Because the vendor is relatively new and independent third-party evidence of churn is limited, plausible drivers for attrition (based on sector patterns) include:
These are hypotheses; procurement should request reference calls with comparable institutions.
What happened (summary): Valparaiso University implemented a prototype faculty expertise directory using the vendor’s platform with a modest pilot cohort of faculty to support matching faculty capabilities to external industry needs.
Reported outcomes (qualitative):
Quantified metrics: public materials did not provide independently verified numeric outcomes such as hours saved, conversion rates, or revenue attributable to the platform.
Implementation details available:
Evidence quality note: parts of the before-and-after comparisons in the institution report are explicitly noted as provided by the vendor, which reduces independence for those specifics.
Scholarly is a B2B, higher-education–focused faculty affairs platform that centralizes faculty data (CVs, activities, documents), supports administrative workflows (e.g., annual reviews; promotion & tenure processes), and provides reporting and analytics for academic leadership. The vendor describes the use of machine learning techniques for tasks such as document parsing, expertise tagging, and natural-language search across faculty records (for example, matching internal expertise to external partner requests).