Publications, datasets, grant records, and expertise live in separate systems — the repository, the research information system, the library, individual labs. None of them holds the connections. LangOptima ingests the scholarly record you already have and connects it into a single knowledge graph — so related findings, prior methods, and potential collaborators surface in seconds, with citations back to the source.
A researcher starts a new project and asks what the institution already knows — prior work, related datasets, who has used this method before. In many research organizations, the answer depends on who they happen to know.
The repository holds papers. The grants office holds awards. Individual labs hold datasets and methods. Each system indexes its own records — and none of them knows what the others contain.
Finding related work inside your own institution still runs on personal networks and keyword luck. Relevant expertise two departments away might as well be at another university.
When a researcher moves on or retires, the connections they carried — which datasets they built on, which methods, which collaborators — leave with them. The papers stay; the context goes.
LangOptima ingests the scholarly record your institution already holds — publications, datasets, grant records, research profiles — and structures what's inside them: topics, methods, materials, authors, and the relationships between them. It sits above your repositories and research information systems without replacing them, so a question that once meant weeks of manual searching becomes a single query with citations.
Repositories, research information systems, grant records, and lab outputs unified at the semantic layer. Nothing moves. Nothing is replaced.
Traverse from a method to every group that has used it, from a dataset to every paper built on it, from a topic to the researchers active in it — connections no keyword search will surface.
“Who here has worked with this technique?” answered in seconds, with the publications to back it — for literature reviews, grant bids, and collaboration decisions.
Every researcher and librarian works with the institution's full memory behind them. New PhD students query decades of institutional output from day one, instead of rediscovering it paper by paper.
Representative scenarios of how research institutions apply a knowledge graph over their scholarly record — illustrative of the pattern, not published client references.
A research office connects its publications, datasets, and grant records in a knowledge graph. Teams starting a new project query prior related work and internal expertise directly — each result citing the publication or dataset — instead of rediscovering externally what a colleague already produced.
A funding call lands and the office needs to know who should bid. Because topics, methods, and people are structured in the graph, the question runs as one traversal — from the call's subject matter to the relevant publications, datasets, and researchers — assembling the evidence base for a bid with its citation trail intact.
However complex the research landscape underneath, the engagement itself stays simple.
We ingest the publications, datasets, and records you already hold, straight from the systems you already run. Nothing is replaced — your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering.
You start asking the questions — and every answer carries a citation back to the source, so you can check it yourself.
A 30-minute conversation about your research information landscape — which systems hold your publications, datasets, and grants, and what a connected view of them would answer first. No deck, no pitch.