The problem
Research ecosystems are dense with activity but opaque in structure. Individual projects, publications, and funding streams are tracked in isolation. Who is working on what, where fields are converging, which capabilities sit dormant — none of it is legible without significant manual effort. The map doesn't exist, so navigation is driven by reputation and chance.
What it does
Research Intelligence maps an ecosystem so its structure becomes visible. Organisations, projects, people, and outputs are connected into a live knowledge graph. The graph shows not just what exists but how things relate — shared methodologies, overlapping domains, citation networks, co-investigator patterns.
The result is a navigable research landscape. A new funder can see where activity clusters and where gaps exist. A research director can track how their portfolio connects to the wider field. A potential collaborator can find the right group without going through ten intermediaries.
The underlying structure
The system rests on a consistent data model: entities (people, organisations, projects, outputs) and relations (co-authorship, funding, thematic overlap, methodological similarity). New data sources — publication databases, grant registries, institutional records — map into the same graph rather than creating parallel silos.
Scope
Concept system applying the Kontai method to the research domain. The core capability — turning a complex relational environment into a navigable, continuously updated map — is the same pattern that runs through Elevator Intelligence and the other systems in this Observatory.