Researchers use Keimenon to organize research, detect patterns, and extract structured outputs.
Researchers use Keimenon to organise large document corpora, detect structural patterns and topic clusters, extract reusable outputs from AI conversations and notes, and publish verified findings with full provenance.
Literature notes in Zotero, drafts in Overleaf, AI conversations in ChatGPT, working notes in Obsidian, data in spreadsheets. Keimenon unifies all of these into one navigable, searchable corpus with automatic topic clustering.
You developed an important idea in an AI conversation, but now it's buried in an archive of hundreds of sessions. Keimenon parses conversation exports and makes every message searchable, clustered by topic, and linked to related material.
Intuition tells you two concepts connect, but proving it across thousands of documents requires systematic analysis. The Knowledge Graph surfaces structural relationships — co-occurrence, reference chains, and thematic overlap — that manual review cannot.
When you synthesise findings into a publication, the chain from claim to source becomes invisible. Keimenon maintains provenance links from published collections back through the extraction chain to the original source material.