# 1.1 Welcome - the data and research intelligence layer for institutional investing
Orbit is the integrated data layer for investment research — one place where the public, internal, and third-party data your team relies on is brought together, linked, and made ready for analysis. It sits between the world's financial information and the decisions your firm makes from it, turning filings, transcripts, research, news, and your own internal documents into a single structured foundation that your analysts, portfolio managers, and AI systems can all work from.
Research data is fragmented by default. Public filings sit in one system, market and pricing data in another, broker research in inboxes and shared drives, news in feeds, and a firm's own notes and models scattered across teams. The work of pulling these together — and keeping them tied to the right company — is where research time is spent, and where it is lost. Orbit's purpose is to be the one layer that ends that fragmentation: every source, public or private, in a single place, anchored to a common backbone, and queryable as one body of knowledge.
That backbone is the **entity master** — a master record of every company and the relationships between them. It is what allows a broker note, a regulatory filing, a news item, and a price series about the same company to be recognised as being about that company, and read together. Connecting a firm's internal research into the global public corpus this way is where the value compounds: your analysts' proprietary view, set against the full public record, in one query. The entity master is the part of Orbit that is hardest to replicate and the reason the platform becomes more useful the more data it holds.
On top of that foundation, Orbit does two things. It lets your team **ask** — putting questions in natural language across all of the data at once and getting answers grounded in primary sources. And it lets your team **automate** — building research as repeatable workflows that run on their own, producing structured data and reports on a schedule or a trigger. The same platform serves the analyst running an ad hoc question this morning and the desk running a standing monitoring process every night.
The coverage beneath it is comprehensive and global. Across more than 60,000 listed companies and over a decade of history, Orbit maintains not just the documents themselves but the structured data drawn from them — spanning the Americas, APAC, and Europe, market by market and country by country. A decade of processed history cannot be back-filled, and it compounds: every new document is read against everything that came before it.
Orbit is also **model-agnostic and personalised by design.** It is not built around a single AI model or vendor. It routes each piece of work to the model best suited to it, balancing quality against cost — so the platform improves as the underlying models improve, and your firm is never locked to one provider's pricing or capability. And it is built so that every user can hold their own logic, their own views, and their own workflows on top of the shared data, rather than being handed a single fixed way of working. The intelligence is in the data, the entity master that links it, and the orchestration around it — not in any one model.
The platform is organised into two layers. The **Knowledge Base** is the foundation: the entity master, the unified body of public, internal, and third-party data, and the structured records built on top. **Orbit Insight** is where your team works: the application and integrations through which you ask questions, monitor companies and portfolios, run and build automated research, and connect Orbit to the systems and data you already use. Part 2 explains how each works, layer by layer.
This is what it means to call Orbit *infrastructure* rather than a tool. It is the layer a firm adopts to bring AI to its research process systematically — across all of its data, in one consistent and governed place — rather than assembling point solutions or asking each analyst to improvise with a general-purpose assistant. The chapters that follow set out exactly what that layer does, and why bringing it all together is a larger undertaking than it first appears.
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# Agent Instructions: 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.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.orbitfin.ai/part-1-what-orbit-is/quickstart.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.