About Labryvanta

Finding the right collaborator is one of the hardest problems in research, and it is largely a search problem in disguise. The person who can help you is often describing the same phenomenon in a different vocabulary — a clinician writes vascular invasion of cartilage, a cell biologist writes angiogenesis, and neither finds the other with a keyword search.

Labryvanta connects researchers, laboratories, universities, engineers and technology companies through a directory that understands meaning as well as words. A search for “blood vessel formation in cartilage” surfaces work on angiogenesis, endothelial signalling and osteoarthritis — none of which contain the phrase.

How matching works

Recommendations combine semantic similarity between profiles with overlap in declared skills, research topics and technologies, alignment of collaboration intentions, and complementary experience — because the value of a match between a PhD student and a principal investigator lies in the gap between them, not the similarity.

Every score is shown with its components and a plain-language reason. A match score is an algorithmic recommendation, not a measure of research quality, and the platform presents it that way on purpose.

What we do not do

AI never silently edits a profile. Anything a model extracts from an uploaded document is a suggestion until the member accepts it, and unapproved suggestions never reach the public profile, the search index, or another member's recommendations.

Members control who sees their profile, and can leave the public directory entirely without losing their account. That setting is enforced in the database query itself, not in the interface.