> 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/3.4-single-name-analysis.md). # 3.4 Single-name analysis #### 3.4 Single-name analysis The single-name use cases cover deep work on one company — the analysis that happens when a name needs real attention, whether because it just reported, because something happened, because the thesis is in question, or because it is a candidate for the book. These reach higher up the value chain than the daily workflow: they are not just access and extraction but analysis and synthesis, judging a company against its own history, its peers, expectations, and a team's existing view. This is where research starts to produce a position rather than a summary. **05 · Earnings call analysis.** What it produces is a worked read of a company's results the moment they land: the numbers against expectations and prior periods, the change in guidance, the substance of management's commentary, and what is new versus what was already known — drawn from the transcript and the release together and grounded in both. The outcome is that an analyst gets to a view on a print in minutes rather than hours, during the window when speed matters most. It runs on demand when a call happens, or automatically the moment a transcript lands for a covered name. On the value chain it spans extraction through analysis — pulling the facts and judging them against context. Run it as shipped, or shape it to test the specific questions your thesis on a name turns on. **06 · Material event flash.** What it produces is a fast, focused read on a material development — an announcement, a disclosure, a piece of breaking news — that sets out what happened, why it matters, and what it bears on, for the company in question. The outcome is that when something moves, a team gets an immediate, grounded assessment rather than waiting to assemble one, and can judge quickly whether it changes anything. Its natural home is the automate mode, triggered by a qualifying event so the flash arrives as the event breaks. On the value chain it is extraction into rapid analysis — turning a raw event into an assessment of its significance. Run it on Orbit's sense of what counts as material, or define the events that matter for your names. **07 · Thesis state monitoring.** What it produces is a standing read on whether the case for holding a name still holds — tracking the developments, results, and disclosures that bear on a stated thesis, and surfacing what supports it and what cuts against it. The outcome is that a thesis stops being something written once and revisited occasionally, and becomes something continuously checked against the evidence, so a team knows when the ground beneath a position has shifted. This is an automate-mode use case by nature, running continuously against a defined thesis. On the value chain it is firmly in synthesis — connecting new information to a team's own view of a company. It depends on the team's thesis being expressed, which makes it one of the most natural to customise: the shipped version monitors against a general frame, but its real power comes when shaped to your actual thesis on a name. **08 · New-name due diligence.** What it produces is a structured first pass on a company a team is considering: the business, the financials, the history, the risks, the open questions — assembled into a coherent starting brief from across all the data. The outcome is that the slow early work of getting up to speed on an unfamiliar name is compressed, so an analyst reaches the point of forming a view far faster, with the groundwork done and sourced. It runs on demand, when a candidate appears. On the value chain it spans access through analysis — gathering everything on a new name and organising it into an assessable form. Run it as a standard diligence template, or customise it to the diligence checklist your firm applies to every new position. The common thread is depth on a single name, produced fast and grounded in sources. Two of these — earnings call analysis and material event flash — are about speed in the moment, getting to a view while the window is open. The other two — thesis state monitoring and new-name due diligence — are about thoroughness, either sustained over time or compressed at the outset. All four climb into the parts of the value chain where judgment matters, which is also why all four reward customisation most: the higher up the chain a use case reaches, the more it benefits from carrying a team's own logic rather than a generic one. --- # 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. 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