Build a private knowledge base from hundreds of papers and notes. Ask questions across your entire corpus — get cited answers backed by exact sources, all on-device with no data risk.
Cloud AI tools create risks that most researchers don't realize until it's too late.
Upload pre-publication research to a cloud AI tool and you risk premature disclosure. Scooping, priority disputes, and IP complications are real — and cloud AI processing creates exposure you cannot retract.
Most universities and research institutions prohibit sending certain categories of research data to third-party cloud services. Grant conditions, IRB protocols, and data-sharing agreements often explicitly restrict this.
Research happens in places without reliable internet — field stations, research vessels, remote locations, secure laboratories. Cloud AI tools are unavailable exactly when you need them most.
Manually reading hundreds of papers to find relevant findings is unsustainable. But uploading your entire literature collection to a cloud AI tool is not a privacy-safe alternative for sensitive research areas.
Index your entire paper collection locally. Ask questions across it from anywhere.
Ask "which papers in my collection discuss the relationship between X and Y?" and get cited answers drawn from across your entire literature base — not just keyword matches, but semantic understanding of meaning and relevance.
Every answer cites its source — paper title, authors, section, and page. No more "an AI told me this" — every claim is traceable to a specific passage in your document collection.
Your entire knowledge base runs on your device. Field station, research vessel, flight, secure lab — Kynora works without any internet connection. Your AI is as portable as your laptop.
Digitize handwritten lab notebooks, scanned conference proceedings, and legacy PDFs. Kynora uses Apple's Vision framework to extract text with high accuracy, making all your legacy research searchable and queryable.
Kynora is designed so no data leaves your device. Pre-publication findings, IRB-covered data, proprietary datasets, grant-restricted research — none of it is transmitted externally. Because all processing is local, Kynora is consistent with the data residency requirements of most institutional policies. Always verify compliance with your institution's specific data governance team.
From literature review to field work — on your device, with full privacy.
Import your entire literature collection and ask "what are the dominant methodologies for X?" Get a synthesized overview with sources to verify — entirely on your device.
Ask "which papers report conflicting results on Y?" Identify areas of genuine scientific disagreement across your literature base instantly.
Working in the field without reliable internet? Your entire paper collection is accessible locally on iPad or Mac, anywhere you go.
Ask "which studies in my collection support the hypothesis that..." and get cited evidence for your application narrative in seconds.
Pre-publication findings, IRB-covered data, grant-restricted research, proprietary datasets — Kynora is designed so none of it ever leaves your device. Your AI works entirely from on-device models and storage. Because all processing is local, Kynora is consistent with the data residency requirements of most institutional data handling policies — but verify with your institution's data governance team before using with regulated research data.
Informational purposes only. The information on this page is provided for general informational purposes and does not constitute compliance, legal, or institutional advice. Whether Kynora's architecture satisfies your institution's specific data governance policies, IRB requirements, grant conditions, or applicable regulations (such as GDPR, HIPAA, or ITAR) is a question you should assess with your institution's data governance office and any relevant compliance or legal counsel. Features described reflect planned launch functionality and are subject to change.
Join the waitlist to be notified when Kynora launches — built for the data-handling standards research demands.