Case study

ACS

Shakewell collaborated with the American Chemical Society (ACS) to develop an intelligent dashboard that streamlines the submission process for researchers. The new Journal Recommender tool uses AI and ML to match authors with the most suitable journals—enhancing both submission volume and accuracy.

At a glance

What it is: the ACS Journal Recommender, an AI-powered submission dashboard
The job: match authors to the most suitable journal from a large catalogue
Why it matters: fewer mismatches, faster submission cycles, fewer rejections and resubmissions

The problem

The American Chemical Society publishes a wide range of scientific journals, but authors often struggled to identify which one best suited their research. The result was mismatched submissions, slower cycles, and a higher rate of rejection and resubmission — costly for researchers and for ACS.

Our approach

We designed and developed the ACS Journal Recommender, an AI-powered dashboard built to encourage submissions through a fast, accessible and intelligent interface.

Features included instant journal search from the homepage, a scrollable interactive showcase of the full journal catalogue, and quick article input fields that feed the recommendation engine — matching authors to journals based on real-time inputs rather than requiring them to know the catalogue.

The outcome

The project blends AI and machine learning with a deliberately simple interface. By recommending the right journal from what an author actually enters, ACS improves both the volume and the accuracy of submissions — shortening the publishing cycle and improving the author experience.

Related services: AI Consulting & Implementation · Technical Publications

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