Our mission
Built for researchers who are tired of managing knowledge in a dozen different places.
Academic research has a knowledge problem. A researcher might spend years accumulating papers, notes, drafts, and ideas — spread across Zotero, Notion, Google Docs, email threads, and dozens of browser tabs. By the time they sit down to write, they've already forgotten half of what they've read.
Academe was built to solve this. It's a single workspace where all of your research lives together — and where an AI that has actually read your materials can help you think through arguments, surface connections, and write with evidence.
What makes Academe different
Most AI writing tools are general-purpose. They respond from broad training data, they hallucinate citations, and they have no memory of what you uploaded last week. Academe is different because it only knows what you have given it. Every answer is grounded in your actual documents. Every citation points to a real page in a real file you uploaded.
The result is an AI that functions less like a search engine and more like a research collaborator — one who has read everything you've read, remembers every argument you've encountered, and can help you write at the pace you think.
Who we're building for
Academe is built for academic researchers — PhD students grinding through dissertations, postdocs managing sprawling literature reviews, faculty drafting manuscripts between teaching and meetings. People for whom the quality of their writing is inseparable from the depth of their reading.
If you've ever lost a great idea because you couldn't find where you wrote it down, or spent an afternoon re-reading papers you already summarized months ago, Academe is for you.
How the AI works
When you upload a document, Academe breaks it into semantic chunks and stores them as vector embeddings — a mathematical representation of meaning. When you ask a question, the system retrieves the most relevant passages from your corpus and passes them to the AI as context. The AI synthesizes an answer from that context alone. It is not trained on your data. It is simply reading it — the same way a research assistant reads a paper before a meeting.
This approach, known as retrieval-augmented generation, means that every response is grounded in text you can verify. If Academe tells you that Smith (2023) defines climate refugees as X, it is because that phrase appears on a specific page of a file you uploaded.
Who Academe is for
The PhD student finishing a dissertation
You have read two hundred papers. Your notes are in six different places. You know the answer is somewhere in your files — you just can't find it. Academe gives you a single workspace that indexes everything and answers questions against your exact corpus.
The postdoc managing a 200-paper literature review
You need to synthesize across sources, identify gaps, and write a coherent narrative under deadline pressure. Academe's AI can tell you what five papers collectively say about a mechanism, or flag which authors disagree — in seconds, not hours.
The research scientist bridging lab work and publication
Your lab notes, draft methods sections, and prior published work all live in separate places. With Academe, your new manuscript draft can cite your own lab notebooks and previous papers as easily as published literature.
Where we're headed
The long-term vision for Academe is an AI that has read every open-access paper ever published — making the entire scientific literature available as context for your work, not just the documents in your personal library. We are building toward collaborative workspaces for research teams, automated citation graph navigation, journal-format export, and integrations with reference managers like Zotero and Mendeley. The goal is a research environment where the distance between reading and writing collapses entirely.