Teamtailor's Co-pilot can suggest candidates from your database for an open job, but only after someone turns the feature on for that specific job and clicks Suggest candidates. It also excludes anyone with no activity in the past year, no matter how strong their old application was. Most closed-job candidates fall outside that window before a team even thinks to look.
The candidate bank underneath all of this is genuinely well organized. Teamtailor tracks duplicates by email, phone, and LinkedIn URL, and lets a recruiter filter rejected candidates for reconsideration. None of it happens on its own. Every step, from enabling Co-pilot to reviewing a duplicate flag, waits for a person to start it.
This guide covers what Teamtailor actually offers for finding candidates already inside your own database. It also covers where that native toolset stops short, and how connecting Skima AI closes the rest of the gap.
What Is Candidate Rediscovery?
Candidate rediscovery means matching people already inside your Teamtailor candidate bank against a role that just opened. It covers applicants from closed jobs, candidates sitting untouched for a year or more, and people your team sourced through the browser extension but never moved into a process.
Your candidate bank becomes a real hiring source once those past candidates get matched against new openings automatically. Teamtailor's search, filters, and segments let a recruiter query that bank by hand. Co-pilot's candidate suggestions can match candidates to a specific job, but only once a person turns the feature on and asks for it.
Does Teamtailor Provide Candidate Rediscovery?
No, Teamtailor does not provide candidate rediscovery or sourcing within the existing database feature. Its candidate bank supports keyword search across resumes, answers, and profile fields, plus filters and saved segments by department, role, or team.
Co-pilot's Candidate suggestions, a paid add-on, go further: once enabled on a job, a recruiter can click Suggest candidates, and Teamtailor searches the wider Talent pool, ranks an initial 20 based on resume and prior review data, then narrows that list using job applications, messages, and comments.
Even this closest native equivalent needs a person at every step. A company admin has to activate Co-pilot, a recruiter has to turn it on for that specific job, and someone has to click suggest candidates before anything happens.
The system also excludes anyone with no activity in the past year and caps suggestions at 20 candidates, so a strong applicant from 18 months ago won't surface no matter how well they'd fit. Nothing scans automatically the moment a job opens, and suggested candidates don't carry a visible match score or an explainable written reason.
How to Rediscover Candidates in Teamtailor
Since Teamtailor's co-pilot suggestions still require a person to enable and trigger them for each job, recruiting teams connect Skima AI to search and score the entire candidate bank at once automatically. Below is a 5-step workflow, from connecting your account to acting on results:
Step 1: Connect Your Teamtailor Account With Skima AI
Generate an Admin API Key inside Teamtailor with read and write scopes on Candidates, Job Applications, Notes, Custom Fields, and Tags. This connection uses Teamtailor's own REST API directly, with no middleware required.
- Read access: Candidate, Job Application, and Job records, plus resume attachments
- Write access: Application custom fields, Notes, and Candidate tags
- Pulled data: candidate contact details, resumes, and job descriptions for scoring context
- Optional webhooks: trigger scoring the instant an application is created, updated, or its stage changes
If webhooks are not enabled, Skima AI falls back to safe polling every 10 to 15 minutes.
Step 2: Configure Custom Fields and Confirm the Connection
Add the Application custom fields Skima AI writes to, or let Skima create them during setup. These are Skima AI Match Score, a number from 0 to 100, and Skima AI Reasons, a short block of reasons. A Skima Screened tag gets added at the candidate level, so screened candidates stay filterable inside the candidate bank.
Optional Hiring Manager Feedback and Comment fields capture manager responses once a shortlist goes out. If custom fields are restricted, Skima AI falls back to writing an application note or attaching a SKIMA_Evidence.pdf instead.
Before going further, test this on two or three job openings. Upload a resume, then confirm the Match Score and Reasons actually populate on that application.
Step 3: Conduct Talent Rediscovery Against a Live Job
Open the job inside Skima AI, from the Jobs list or from within the job itself. Click the three-dots Actions menu and select Rediscover Candidates. A modal lets you scope the scan two ways. Limit it to a saved candidate segment, or leave it open to every candidate record Skima AI has synced from Teamtailor.
Set the Candidate Created Date range next: All time, Last 6 months, Last 1 year, or a custom window. Click Apply. Skima AI scans the selected pool against this job's specific requirements and sends a notification once the scan finishes. Refresh the page to see the results.
Step 4: Review and Filter the Rediscovered Matches
Open the View Candidates tab to see a ranked list pulled from across your entire candidate bank, including candidates Teamtailor's own Co-pilot would have excluded for a year of inactivity. Each candidate carries an AI match score and reason bullets explaining the fit. Filter by Industry, Work Mode, Experience Level, Notice Period, Location, and Salary Range.
Start broad and tighten the filters gradually. Narrowing too fast hides good candidates. This step often catches applicants who were weak fits for an old role but line up well against this new one.
Step 5: Shortlist and Sync Feedback Back to Teamtailor
Build your shortlist from the ranked matches Skima AI surfaced. Generate a secure, no-login link and send it to the hiring manager. They can click a thumbs up, thumbs down, or maybe on each candidate, with an optional comment.
Skima AI writes that decision back into Teamtailor through the same API connection for the audit trail, logging the feedback as an application note so it stays on the record without a second tool.
What If Your Teamtailor Database Is Messy, Duplicated, or Stale
Teamtailor already does more here than most ATS platforms. It flags a candidate as a duplicate automatically when their email, phone number, or LinkedIn profile URL matches another record, and a dedicated Duplicated filter shows every flagged pair in one list.
Opening a candidate card and clicking Review and merge highlights exactly which fields match, in yellow, before anything gets combined. A separate Rating filter surfaces candidates with low scores, and a Rejected filter isolates candidates worth reconsidering for a different role.
None of this happens without a recruiter opening the filter and choosing what to do next. Flagging is automatic, but merging, rejecting, and reviewing still take a person clicking through the list. On an account with years of candidates across many jobs, that list gets long enough that most teams only get to it occasionally, if at all.
Skima AI treats this as ongoing maintenance rather than a one-time cleanup project. It matches on email, phone, or LinkedIn URL first, then checks name, location, and company together as a backup signal.
A field comparison modal flags exactly what differs before anything merges; missing details get filled in through contextual profile enrichment, and a dedicated Duplicates tab logs every resolved case across In Database, From Uploads, and Resolved sub-tabs.
5 Benefits of Candidate Rediscovery with Skima AI in Teamtailor
Talent Rediscovery with Skima AI changes what a Teamtailor team can do with its own candidate bank in 5 specific ways:
- Faster Time-to-Screen: Rediscovered candidates arrive already scored, so recruiters skip the raw resume pile entirely. In a 30-day Teamtailor pilot, a team cut time-to-screen by 58%.
- More Interviews From the Same Shortlist: The same pilot saw interviews from the top shortlist double compared to the prior process.
- A Wider Net Than Co-pilot's Suggestions: Skima AI's scan reaches candidates with no activity in over a year, the exact group Teamtailor's own Candidate suggestions feature leaves out by design.
- Explainable Match Scores With an Audit Trail: Every rediscovered match carries reason bullets tied to skills and experience, plus a Skima Screened tag for instant filtering inside the candidate bank.
- Hiring Manager Engagement Without a New Login: The shortlist feedback loop keeps managers responding through a link, no Teamtailor credentials required. Manager satisfaction rose 24 points on NPS in that same pilot.
5 Best Practices to Rediscover Talent in Teamtailor
None of these benefits happen automatically inside Teamtailor without a few deliberate habits. The 5 practices below cover search timing, data hygiene, and fitting rediscovery around tools your team already uses:
- Use Co-pilot's Suggestions First, Then Widen With Skima AI: If Co-pilot is enabled, check its suggestions before waiting on a full scan. It won't catch anyone inactive for over a year, so let Skima AI's Talent Rediscovery cover that gap on the same job.
- Turn On Automatic Rediscovery for Every New Job: Skima AI's setting under Preferences and General starts a database scan the instant a new job is created. Enable it so no opening slips through without a first look at your existing candidates.
- Clear the Duplicated Filter Before Running a Large Scan: A candidate counted twice skews results and wastes a hiring manager's review time. Resolve Teamtailor's own Duplicated filter on a regular schedule, not just when Skima AI flags a repeat.
- Use Reverse Search for Candidates Sourced but Never Processed: People added through Teamtailor's browser extension often sit without a job attached. Reverse Search starts from one profile and returns every open job they match, ranked by score.
- Segment Scans With the Candidate Created Date Filter: Choose a 6-month or 1-year window when a role needs someone recently active. Widen to All time for a niche skill set where the pool is thin.
Is Candidate Rediscovery Worth It for Your Recruitment Team?
Setup is the first real cost. An Admin API key, custom field mapping, and webhook configuration require significant IT time before any candidate is scored. Skima AI is also an additional paid layer (though affordable) on top of whatever Teamtailor tier your team already pays for. Whether it's worth the cost depends on what's in your candidate bank.
A team with years of applications, several closed jobs each year, and a Co-pilot suggestion pool that already excludes anyone inactive for 12 months has real unscored value that remains untouched. A newer team with a short hiring history and a small candidate bank has less to gain, at least for now.
Rediscovery becomes important once the gap between what Co-pilot can access and what your full candidate bank actually contains is wide enough that a person can't bridge it by memory.
Final Verdict: Pilot Candidate Rediscovery in Teamtailor Now
For most established Teamtailor accounts, the case for piloting is strong. The right move is not a company-wide rollout on day one. Start with three live jobs already struggling through normal sourcing.
Connect Skima AI, configure the application custom fields, and launch Talent Rediscovery on those three jobs. Skima AI's own Teamtailor integration structures this as a 30-day pilot, with weekly ROI emails and checkpoints at day 15 and day 30.
Track time-to-screen, how many rediscovered candidates reach an interview, and hiring manager response. Based on a 30-day Teamtailor pilot, expect something close to a 58 percent drop in time-to-screen and double the interviews from your top shortlist. From there, expanding to more jobs becomes a decision backed by your own data, not a guess.
Frequently Asked Questions
1. Does Teamtailor have a built-in dedicated candidate rediscovery feature?
No, Teamtailor does not have a built-in dedicated candidate rediscovery feature. Instead, it offers Co-pilot Candidate suggestions, a paid add-on enabled per job and triggered manually, plus native duplicate detection. It excludes anyone inactive for a year and caps results at 20, so it never covers the full candidate bank automatically.
2. How to conduct candidate rediscovery in Teamtailor ATS?
To conduct candidate rediscovery in Teamtailor ATS, connect Skima AI to Teamtailor using an Admin API Key, configure Application custom fields, then launch Talent Rediscovery on a live job. Skima AI scans your entire candidate bank, including candidates Co-pilot excludes, and syncs hiring manager feedback automatically.
3. What are the benefits of Teamtailor candidate rediscovery?
Rediscovered candidates arrive pre-scored, cutting screening time by over 58% in a 30-day pilot and doubling interviews from the top shortlist. Skima AI reaches candidates inactive for over a year, which Teamtailor's own Co-pilot suggestions exclude by design.
4. Is Skima AI safe to integrate with Teamtailor for talent rediscovery?
Yes, Skima AI encrypts candidate data in transit and at rest, operates under a signed Data Processing Agreement, and follows SOC 2 practices. Match scores use resume-based evidence only, with no automatic rejections, and every decision stays logged for audit.
5. Is candidate rediscovery useful for Teamtailor recruiting teams?
Yes, especially for teams with years of candidates across many closed jobs. Teamtailor's own Co-pilot only searches when enabled and excludes anyone inactive for a year, so rediscovery's value grows the larger and older your candidate bank becomes.
