# Where Candidates Come From

Source: https://docs.mira.day/en/docs/match-quality-and-trust/where-candidates-come-from

> The public talent data and continuously updated index Mira reasons across, how broad the coverage is, and how to read a profile's freshness.



Mira reasons over the best available public data about people and companies. It doesn't rely on a single source; it draws on several kinds of public professional data, then evaluates what it finds against your criteria. Here's where that data comes from.

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What Mira draws on [#what-mira-draws-on]

| Source                     | What it covers                                                                                                                                                                                               |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Public talent data**     | Aggregated from multiple public talent data sources, such as LinkedIn, X, GitHub, and Hugging Face, plus academic papers and company information. Covers industries, regions, and seniority levels globally. |
| **Mira's own index**       | A continuously updated index of public professional profiles and job postings.                                                                                                                               |
| **Mira Browser Extension** | For sites behind a sign-in or not in the index, [the extension](/en/docs/tools-and-teamwork/mira-browser-extension) navigates them in your own browser, on demand.                                           |

Mira reasons across all of these together, so a candidate can surface from evidence spread over more than one source, not just whoever happens to match a single database.

How broad the coverage is [#how-broad-the-coverage-is]

Coverage is global, across industries, regions, and seniority levels, from early-career to executive. Mira's index spans 4.5 billion public records. What matters for a given search is less the raw size and more whether the people who fit your criteria are represented, which is why reasoning over the evidence, rather than keyword-matching a single list, is what surfaces them.

How current the data is [#how-current-the-data-is]

Public data ages, people change jobs, and profiles aren't always updated the day they do. So Mira shows a **freshness badge** on each candidate, marking when that profile's data was last updated, for example "Updated · 3w ago", with the exact date on hover. When an update date isn't available, no badge is shown rather than a guess.

Use it as a signal: a very fresh profile is more likely to reflect someone's current role, while an older one is worth a second look or a quick confirmation before you reach out. You'll see the same badge on the candidate card and in the full profile.

What this means for your search [#what-this-means-for-your-search]

Because Mira reasons over evidence rather than pulling from one list, two things follow. If a search returns fewer people than you asked for, that reflects the available data for those criteria, not a hidden cap, loosen a requirement and you'll usually see more. And accuracy tracks how complete the public data is: the more a profile actually says, the more precisely Mira can assess it. For how that assessment works, see [How Mira Matches Candidates](/en/docs/match-quality-and-trust/understanding-ai-matching).

What's next [#whats-next]

* [How Mira Matches Candidates](/en/docs/match-quality-and-trust/understanding-ai-matching): how Mira reasons over this data to score each candidate.
* [Inside a Candidate Profile](/en/docs/sourcing/candidate-profile): what each field on a candidate means, including the freshness badge.
* [Mira Browser Extension](/en/docs/tools-and-teamwork/mira-browser-extension): reach sites and sign-ins that aren't in the index.