> 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/3.3-daily-workflow.md). # 3.3 Daily workflow The daily workflow use cases cover the recurring rhythm of a research day — the standing tasks that frame everything else: knowing what changed overnight, knowing what is coming, and clearing the noise to find what matters. These are the use cases that benefit most from running automatically, because their value is in being done before the day starts rather than on demand. All four sit at the foundational end of the value chain — access and extraction — but done continuously and across a whole portfolio or coverage list, which is what turns a morning of catching up into a morning already caught up. **01 · Morning portfolio brief.** What it produces is a single read on the state of a portfolio at the start of the day: what moved, what was announced, what news broke across the holdings, and what deserves a closer look — drawn together from across all the data and tied to the right companies through the entity master. The outcome is that a PM or analyst begins the day already oriented, rather than spending the first hour assembling a picture from scattered sources. Its natural home is the automate mode: defined once against a portfolio, it arrives every morning before the desk sits down. On the value chain it is access and extraction, but synthesised into one brief rather than left as a pile of separate alerts. Run it as-is against your book, or shape it to lead with the metrics and events your desk cares about most. **02 · Pre-earnings calendar prep.** What it produces is a forward view of the reporting season: which covered companies report when, and what to be ready for in each — the prior quarter's open questions, the guidance to test, the points the last call left unresolved. The outcome is that a team walks into earnings season prepared rather than reacting to it, with the prep work done in advance instead of scrambled together the night before each print. It runs naturally on a schedule ahead of the season and refreshes as dates firm up. On the value chain it is access and extraction in service of synthesis — assembling what is known so the team is ready to judge what is new. Run it across your coverage, or customise the prep checklist to your house process. **03 · Weekend research prep.** What it produces is the queued-up reading and analysis a team wants ready for the week ahead: the filings, developments, and open items across the coverage universe, organised so that Monday starts from a prepared position rather than a backlog. The outcome is that the gap between Friday and Monday stops being a blind spot — the platform does the catching-up over the weekend so the team does not have to. It is an automate-mode use case by nature, running over the weekend to deliver a ready brief. On the value chain it is foundational work done while no one is watching. Run it as shipped, or tune what it gathers and how it prioritises. **04 · News flow triage.** What it produces is a filtered, prioritised view of the news across a portfolio or universe — separating what is material from what is not, and surfacing the items that bear on a team's specific holdings and theses. The outcome is the recovery of the time research teams lose to scanning feeds: instead of reading everything to find the few things that matter, a team is brought the few things directly, each tied to the right company. It works on demand — "what's material right now" — but its natural home is a standing watch that triages continuously through the day. On the value chain it is access and extraction with a layer of judgment about materiality. Run it with Orbit's default sense of what counts as material, or sharpen that definition to your own. Across the four, the through-line is that a research day has a fixed amount of orienting and catching-up built into it, and these use cases move that work off the team's plate and into the background. None of it is the work that creates edge — it is the work that has to happen before the edge-creating work can begin. Done by the platform, on a schedule, it gives a team back the start of its day. --- # 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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