> For the complete documentation index, see [llms.txt](https://docs.orbitfin.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.orbitfin.ai/part-3-use-cases/3.6-thematic-and-strategic.md). # 3.6 Thematic and strategic The thematic use cases organise research around themes rather than individual names — a structural shift in approach. Instead of starting from a company and asking what it does, they start from an idea — an industry shift, a technology, a policy direction, a structural trend — and ask which companies are exposed to it, how much, and what is happening to it. This is where the relationships in the entity master do their most visible work: a theme is not a list of companies someone tagged, but a structure that can be traced across the connections between companies, including the indirect ones. On the value chain these reach into synthesis, connecting developments across many companies to a theme a team cares about. **13 · Thematic exposure.** What it produces is a map of which companies are exposed to a given theme and to what degree — not just the obvious names everyone associates with it, but the fuller set, weighted by how much of each business actually touches the theme. The outcome is that a team can see a theme as an investable universe rather than a handful of household names, and judge real exposure rather than reputational association. It runs on demand to assess a theme, or on a schedule to track how a theme's universe evolves. On the value chain it is analysis and synthesis across many companies at once. Run it on Orbit's reading of a theme, or define the theme precisely as your team frames it. **14 · Theme contributor ranking.** What it produces is a ranking of the companies most exposed to a theme by the degree of their contribution — sorting an investable universe by who is genuinely a play on the idea versus who is only loosely connected. The outcome is that a team moves from "these companies relate to the theme" to "these are the ones where the theme actually drives the business," which is the distinction that matters for position-taking. It runs on demand and refreshes as the underlying exposures change. On the value chain it is synthesis with a layer of judgment about degree and materiality. Run it as shipped, or shape the contribution criteria to what your team counts as real exposure. **15 · Hidden beneficiary discovery.** What it produces is the non-obvious set: the companies that stand to benefit from a theme without being labelled as theme names — the suppliers, the enablers, the second-order beneficiaries that surface only when the connections between companies are followed rather than their headline classifications. The outcome is the kind of finding that is genuinely differentiated — exposure others miss because it does not show up under the obvious search. This is where the entity master's relationship structure pays off most directly: hidden beneficiaries are found through links, not labels. It runs on demand as a discovery exercise. On the value chain it reaches into synthesis and toward the edge-creating end of it. Run the discovery broadly, or direct it along the specific kinds of relationship your team wants to trace. **16 · Theme event tracking.** What it produces is a standing watch on a theme: the developments, announcements, and data points across the theme's whole universe that signal it strengthening, weakening, or shifting — aggregated into a continuous read on the theme rather than scattered across the news of individual names. The outcome is that a team holding a thematic view knows when the evidence for that view is changing, across all the companies the theme touches at once. It is an automate-mode use case by nature, running continuously against a defined theme. On the value chain it is synthesis sustained over time. Run it on Orbit's framing of the theme, or track exactly the signals your view depends on. The four form a natural sequence: map a theme's exposure, rank who genuinely drives it, discover the beneficiaries others miss, then track the whole thing over time. Hidden beneficiary discovery is the one to dwell on — it is the clearest example in the entire library of a use case that produces something hard to get any other way, because it depends on the relationship structure underneath the data rather than on search. That is a useful illustration of the broader point: the more a use case relies on the connections in the entity master rather than on documents in isolation, the more differentiated its output, and the harder it would be to reproduce without the backbone. Thematic research is where that advantage is most visible. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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