> 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-1-what-orbit-is/1.4-capabilities-at-a-glance.md).

# 1.4 Capabilities at a glance

At a high level, Orbit gives a research team five things to do with its data. Each is covered in depth later — the mechanics in Part 2, the applied use cases in Part 3 — but together they describe the shape of the platform, and the order in which a firm typically grows into it.

**Access.** Orbit puts a comprehensive, global body of research data within reach from one place: more than 60,000 listed companies across the Americas, APAC, and Europe, over a decade of history, spanning filings, transcripts, sustainability and regulatory documents, news, and market data — alongside whatever internal and third-party sources the firm brings in. Everything is anchored to the entity master, so it is not just available but linked. Access, in Orbit, means access to *connected* data, not another set of silos to search.

**Ask.** Your team puts questions in natural language and gets answers drawn from across all of that data at once — grounded in primary sources and traceable back to them. Because the documents have already been read and structured, an answer about a company draws on its filings, its disclosures, the research and news about it, and the firm's own notes, read together. This is the ad hoc mode: a question this morning, answered now.

**Monitor.** Beyond one-off questions, Orbit keeps companies, portfolios, and themes under continuous watch. Rather than re-running the same checks by hand, a team sets what matters — holdings, a coverage universe, a thesis, a theme — and Orbit surfaces what changed and what warrants attention. Periodic review becomes standing awareness.

**Automate.** The same agents that answer questions can be assembled into repeatable workflows that run on their own — on a schedule or a trigger — producing structured data and finished reports without anyone restarting the process each time. Orbit ships with off-the-shelf agents to start fast, but the larger idea is that every user can build their own: encoding their logic, their views, and their workflow, and eventually linking agents together to automate research end to end. This is where a team moves from using AI occasionally to running its research process on it.

**Integrate.** Finally, all of the above is built to live inside the firm's existing environment rather than beside it. Orbit connects to internal systems and third-party data, is reachable through an API and standard connectors, and can be deployed privately where required — so the firm's own AI investments and existing tools draw on the same connected data foundation, governed in one place.

Read together, these are not five separate features so much as one progression: a firm starts by *accessing* connected data and *asking* questions of it, grows into *monitoring* and *automating* its recurring research, and *integrates* the whole layer into how it already works. That progression — from occasional use to systematic infrastructure — is the path the rest of this documentation follows.


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