# 1.2 Who Orbit is for

Orbit is built for the institutional buy-side — hedge funds, asset managers, and the investment teams within them — where research depends on reading across a large, fragmented body of information and turning it into decisions. It is designed to meet a firm wherever it sits in its own adoption of AI, rather than assuming everyone starts from the same place. A team taking its first structured step beyond a general-purpose assistant and a desk already running automated workflows are both served by the same platform; what differs is how much of it they switch on.

The same foundation looks different depending on the role using it. Because every source sits in one place, anchored to the same entity master, each person works against the same underlying data — but through the lens of their own job, their own coverage, and their own logic.

**The research analyst** uses Orbit to cover more ground without losing depth. They pull structured fundamentals across a coverage universe, track how a company's disclosures and guidance evolve over time, read a company's filings alongside the broker research and news about it, and produce first-draft analysis grounded in primary sources rather than starting from a blank page. The analyst can also build agents that encode their own approach — the metrics they watch, the questions they always ask of a new filing — so their method scales beyond what they can read by hand.

**The portfolio manager** uses Orbit to keep a book under continuous watch. They monitor holdings against theses, see what has changed since the last review, and get synthesis that connects a single company's news to the wider position — without waiting for the next scheduled update. Standing monitoring workflows run on their own and surface only what warrants attention.

**The head of research** uses Orbit to scale and standardise the function. They apply consistent process across the whole team, codify the firm's research approach into reusable agents and workflows, and extend coverage into markets and asset classes that were previously out of practical reach for the headcount available. Where each analyst once held their method in their head, the head of research can see it captured, shared, and run at scale.

**The quantitative team** treats Orbit as a clean, entity-anchored data source. Structured fields, linked to the right company and traceable back to the originating document, accessible by API — suitable for signal construction, systematic screening, and continuous monitoring across a wide universe.

**The data or technology leader** treats Orbit as infrastructure. Rather than commissioning an internal build to unify the firm's research data, they adopt a layer that already connects public, internal, and third-party sources, plugs into existing systems through the API and standard connectors, and can be deployed privately where required — giving the firm's own AI investments an institutional-grade data foundation to draw on, governed in one place.

These are not separate products. They are the same Knowledge Base and the same Orbit Insight, surfaced for different work and shaped by each user's own logic and views. That is what makes Orbit a single layer a whole firm can adopt — analysts, PMs, quants, and technologists working over one shared, linked body of data — rather than a collection of disconnected tools, each team solving the integration problem again on its own.


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