> 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-2-how-it-works/2.2-the-entity-master.md).
# 2.2 The entity master
Every other part of Orbit rests on one thing: a master record of every company Orbit covers, and the relationships between them. This is the entity master. It is the least visible part of the platform and the most important — the backbone that turns a collection of separate sources into a single connected layer.
The problem it solves is deceptively hard. A single company appears across the world's data under many different names and identifiers: a legal name in a filing, a ticker on an exchange, a shortened name in a news headline, a subsidiary in a broker note, a different romanisation in another market. To a person, these are obviously the same company. To a system, they are not — unless something reconciles them. The entity master is that something. It resolves every document, every data point, and every reference to the one company it actually concerns, so that everything Orbit holds about that company can be brought together and read as one.
> *Caption: Many sources, many names, one company — reconciled by the entity master.*
This is what makes Orbit *the* data layer rather than one more source to reconcile. Because every source is anchored to the same backbone, a filing, a news item, a price series, and a firm's own internal research about a company are recognised as belonging together — and can be queried together. Ask about a company and the answer draws on everything Orbit knows about it, from every source, without anyone first having to work out which records refer to which entity.
The relationships matter as much as the records. The entity master holds not just companies in isolation but how they connect — corporate structures and the links between related entities — so that analysis can follow those connections rather than stopping at a single name. This is the foundation that later chapters build on: it is what lets monitoring, screening, and thematic work reach across a universe of companies rather than examining them one at a time.
The backbone is also what makes integrating a firm's own data so valuable. When internal research — broker notes, meeting notes, proprietary models — is brought into Orbit, it is anchored to the same entity master as the public corpus. A firm's private view of a company then sits directly alongside the full public record of that same company, in one place, in one query. This is covered in detail in 2.7, but the reason it works at all is the backbone: without a common entity master, internal and external data are two disconnected piles; with it, they are one connected body of knowledge.
This is also the part of Orbit that is hardest to replicate, and the clearest answer to why building this in-house is a larger undertaking than it appears. Wiring a model to a database is quick. Building an entity master that reliably resolves every document and data point to the right company — across markets, across languages, across name changes, mergers, and corporate restructurings, and keeping it correct as companies change over time — is slow, painstaking, and never finished. It is not a feature that a larger or better AI model makes easier, because it is a data problem, not a reasoning problem. A firm that sets out to unify its research data discovers that this backbone is most of the work, and that the work does not end. Orbit has already done it, and maintains it continuously.
Everything that follows in this section — the unified data layer, the structured records, the agents — depends on this foundation. It is the reason Orbit becomes more useful the more data it holds: each new source is not another silo, but one more body of information linked into a structure that already understands the companies it describes.
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