The gap between raw insight and structured knowledge is the single largest inefficiency in modern research. Researchers spend 40% of their time formulating queries, filtering noise, and manually mapping citation networks. This is not research. This is overhead.
Cognir Research eliminates the overhead.
How it works
Input your unstructured notes, half-formed hypotheses, or stream-of-consciousness observations. The engine extracts your underlying research intent, validates it against live academic literature, and returns a curated reading list with ranked relevance, identified gaps, and an optimal reading sequence.
How does the dual-engine framework actually decide when to pull from a researcher’s own notes versus outside literature, and does it let you override those choices when you already know the angle you want?
How does the dual-engine framework actually pull and rank literature, and does it lean on specific databases or scrape open sources on its own?
How does the dual-engine actually pull and rank the evidence, and is it pulling from the open web or only from a curated corpus? Curious how it handles paywalled sources.
A citation export to BibTeX or RIS would be huge, since most of us drop straight into Zotero or EndNote after the literature pass. Right now I can see the curated papers but retyping the references kills the flow you just built. Would love that little export button at the end of a pathway.
How does it actually rank the questions it surfaces, and can I see why one beat out the others?