Academic researchers increasingly need public visibility. Grant funders, tenure committees, and institutions now reward broader impact, but most scientists lack the time or skill to translate dense papers into engaging social content. Writing a thread or LinkedIn post from a 30-page paper is slow, and generic AI tools strip out nuance or hallucinate claims, which is a career risk when your reputation rests on accuracy.
Marky for Science Communicators solves this. A researcher uploads a paper (PDF or DOI), and the product generates a full week of platform-specific posts: X threads, LinkedIn posts, Bluesky, and short-form scripts. Every claim is tied back to the exact section of the source paper, with accurate citations, so the scientist can verify before publishing. The core promise is not just content but trustworthy content that will not embarrass the author.
The MVP is narrow: PDF upload, extraction of key findings, generation of a 7-post batch per platform, inline citation anchoring to the source, and one-click edit/export. No scheduling or analytics at first. This keeps the build focused on the differentiator, which is fidelity to the paper.
Revenue is a straightforward SaaS subscription: a monthly tier for individual researchers and communicators, with a higher tier for labs, university comms offices, and science media teams that manage multiple authors. GTM starts inside science communication communities and courses (the SciComm crash course audience), academic Twitter/Bluesky, and university communications departments who already do this work manually.
Growth loops come from the output itself: every published post is a live sample of the product, and citation-linked posts can carry subtle attribution. Labs that adopt it pull in co-authors, and university comms teams become multi-seat accounts that expand across departments.