Our Mission
The volume and velocity of research has outpaced our ability to make sense of it.
Every year, millions of papers are published across tens of thousands of journals, preprint servers, and institutional repositories. Research fields fragment into sub-disciplines. Terminology shifts. Funding priorities evolve. Emerging topics, paradigm shifts, and cross-disciplinary opportunities stay buried in the volume.
The tools available to research organizations have not kept pace: keyword search, citation counts, journal rankings, and the intuitions of individual experts. These were built for a different era of scholarly output. They surface information, but they were never designed to support decisions at today's scale and complexity.
Tasseo exists to change that.
Our Platform
Tasseo is a research decision platform for organizations that fund, produce, and evaluate science. We transform the global body of scholarly knowledge into structured intelligence, then translate it into insights that drives action.
Our platform covers the research landscape end-to-end: a continuously updated topic ontology that maps how science is structured and evolving; trend analytics that identify which fields are accelerating and why; and predictive models that surface where research is heading before field consensus forms. Each layer is designed to inform a specific decision: funding allocation, research portfolio strategy, partnership evaluation, or competitive positioning. Together, they form a unified decision foundation.
Our Approach
Data shown is not the same as a decision made. The measure of our platform is what organizations are able to decide and act on with confidence. That requires structure. We model research as a knowledge graph, mapping topics, entities, methods, and their relationships, so that every query, every visualization, and every recommendation is derived from deterministic models based on graph structures (topic hierarchies, entity relationships, methodological lineages) rather than surface-level text similarity or pure LLM based approaches that are error prone. Our methods are documented, sources are traceable, and outputs are designed to be questioned. Human judgment stays at the center of every decision; AI-generated insights are the evidence layer beneath it.
Organizations trust us with consequential choices. Our data stewardship practices, access controls, and provenance infrastructure are built for the compliance and integrity requirements research organizations demand.