> ## Documentation Index
> Fetch the complete documentation index at: https://help.equip.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Talent Rediscovery

> Search every past applicant across your job openings and re-rank them against a new role.

Your best candidate for a new role may have already applied to an old one. **Talent Rediscovery** searches every candidate who has ever applied to any of your team's job openings and ranks them against the role you are hiring for now.

Open it from the sidebar under **ATS → Talent Rediscovery**. The page shows how many past candidates your team has, so you know the size of the pool before you start.

<Note>
  Talent Rediscovery is part of Equip's free ATS. Searching and ranking your past candidates does not consume any credits.
</Note>

## How Rediscovery Works

There are two ways to start a search. You can upload a **job description**, which opens a modal where you paste the JD and Equip extracts the search criteria for you. Or you can set a **skill filter** directly and refine from there.

<Steps>
  <Step title="Open Talent Rediscovery">
    In the sidebar, go to **ATS → Talent Rediscovery**. The empty state shows your team's total number of past candidates.
  </Step>

  <Step title="Set your criteria">
    Click **Upload a Job Description** and paste the JD so Equip can extract criteria, or set a skill filter above the results to begin.
  </Step>

  <Step title="Review the ranked results">
    Once a skills filter is set, each candidate receives a **relevance score**. Scan the ranked list and refine your filters as needed.
  </Step>

  <Step title="Open a candidate's detail panel">
    Click a candidate to see their full application history across all your openings, including the stage and fit score of each application, plus their work and education history.
  </Step>
</Steps>

Each result row packs in everything you need to judge a candidate without opening their profile:

| Column             | What it shows                                                                    |
| ------------------ | -------------------------------------------------------------------------------- |
| Contact info       | Name and contact details                                                         |
| Experience         | Estimated current experience alongside the original value from their application |
| Skills             | Per-skill years (aged) and expertise level                                       |
| Latest role        | Most recent role and company                                                     |
| Latest application | The job opening they last applied to and the stage they reached                  |
| Source             | How the candidate entered your ATS                                               |
| Activity           | Whether they attempted an assessment or AI interview                             |
| Relevance score    | Overall score with a skills, experience, and recency breakdown                   |

## Scoring Weights

Relevance scoring activates as soon as a **skills filter** is set. Every candidate in the results is scored on three components, and you control how much each one counts.

| Component      | Default weight | Editable |
| -------------- | -------------- | -------- |
| **Skills**     | 50%            | Yes      |
| **Experience** | 30%            | Yes      |
| **Recency**    | 20%            | Derived  |

The three weights must always sum to 100. You edit Skills and Experience directly, and **Recency** takes whatever remains.

<Tip>
  Hiring for a fast-moving stack? Lower the Experience weight so Recency rises, and recent applicants with fresher skills float to the top.
</Tip>

Each candidate's relevance score comes with a per-component breakdown, so you can see whether a high ranking is driven by skill match, depth of experience, or how recently they applied.

## How Experience Is Estimated

Candidates keep gaining experience after they apply, so Talent Rediscovery ages their numbers forward. A banner on the page reminds you: "Experience and per-skill years are estimated based on time since each candidate applied."

The estimation works in two parts, explained in the **How experience is estimated** modal on the page:

* **Total experience** grows by the time elapsed since the candidate applied.
* **Per-skill years** grow proportionally to that same elapsed time.

<Warning>
  Aged figures are estimates, not facts. A candidate may have switched stacks or taken a break since applying. The results show both the aged and the original values, so always confirm current experience with the candidate before making decisions.
</Warning>

## Exporting Results

Once you have a ranked list you are happy with, you can **export the results** to work with them outside Equip.

<Tip>
  Export a rediscovery run before opening a new role to build a warm longlist for your hiring manager. Reaching out to past applicants is usually faster and cheaper than sourcing cold candidates.
</Tip>

## Related Resources

* [Candidate Search](/candidate-search) - Look up a specific candidate by name or email across all activity
* [Job Fit Score](/job-fit-score) - How Equip ranks active applicants within a single job opening
* [Candidate Profile](/candidate-profile) - What lives in a candidate's detail panel and journey
* [Bulk CV Import](/bulk-cv-import) - Grow your talent pool by importing up to 100 CVs at once