Most recruiting teams treat every new requisition as a search that starts from zero. A job goes live, and the default move is to post it and wait, even when a similar role closed six months earlier with several strong finalists still on file. Those finalists rarely come up again unless someone remembers a name.
This expert guide explains what Workable does with that stored history once a new job opens, and how integrating an external AI tool turns it into a scored, repeatable check rather than something a recruiter has to remember to look for.
What Is Talent Rediscovery?
Talent rediscovery means searching and matching people already stored in a company's ATS against a role that just opened. It covers applicants from closed jobs, candidates parked in a talent pool, and anyone who advanced partway through a hiring process before the role got filled or shelved.
A year or two of application history turns into an active sourcing channel once those stored candidates get checked against every new opening automatically. Most ATS platforms leave that to a recruiter's memory. Rediscovery performs the check every time, without anyone needing to recall a name.
Does Workable Provide Candidate Rediscovery?
Partially, Workable's Resurface Candidates tool searches a company's entire database, including its Talent Pool, for candidates that match a specific open job.
A recruiter opens the job, selects Resurfaced Candidates, and clicks Search with AI to load matches in batches of five. These matches are based on keywords, experience, seniority, location, and past evaluations. This is different from People Search, which looks at external profiles rather than the company's own history.
A recruiter still needs to open each job and manually trigger the search. Matches come with descriptive labels, like Competency or Past favorite, instead of a numeric score. The tool will not search for the same job again within a month unless the requirements change.
Anyone added to the database after the first search remains unchecked against the job until the month passes, and there is no connection between hiring manager feedback and the results.
How to Rediscover Candidates in Workable
Workable's native tools give a recruiting team a manual head start on rediscovery. However, to move faster and fairer, recruiting teams connect Skima AI to score and rank that same candidate history automatically and keep the scan going past Resurface Candidates' one-month limit.
Below is a 5-step workflow that starts with what Workable already provides and builds on it:
Step 1: The Native Path, Start With Resurfaced Candidates
Open a job from the main dashboard and click Find Candidates, then choose Resurfaced Candidates. Click Search with AI to preview matches, loaded in batches of 5 and pulled from every other job and the Talent Pool in the account.
Matched profiles get tagged #resurfaced_candidate and #suggested_by_workable, and each one carries labels explaining the match, such as Competency or Culture fit, rather than a number.
This step surfaces real candidates from a company's own history without a manual search through the Candidates page. It gives no numeric score, though, and it cannot be triggered again for the same job within a month unless the job changes. For a repeatable, scored version of this same search, Skima AI builds on this next.
Step 2: Connect Your Workable Account With Skima AI
Generate an API token with Super Admin access, scoped for r_candidates, r_jobs, w_candidates, and w_comments. This connection uses Workable's own API directly, with no middleware required.
- Read access: candidate profiles, application data, job details, and resume attachments
- Write access: custom attributes and comments
- Pulled data: candidate contact details, resumes, application stage and source, job descriptions, and pipeline context for scoring
- Event triggers: candidate created, candidate updated, and candidate moved, delivered through Workable's webhooks
Workable enforces a rate limit of 10 requests per 10 seconds. Skima AI manages this with queued, batched requests so scoring keeps up without triggering throttling.
Step 3: Configure Custom Attributes and Confirm the Connection
Create custom attributes for Match Score, Reasons, and Shortlist Link inside Workable, or let Skima AI provision them automatically. If webhooks are not enabled for candidate created, updated, and moved events, Skima AI falls back to a 10 to 15 minute polling interval instead.
Before rolling this out further, test the connection on two or three requisitions. Upload a resume, confirm the Match Score and Reasons appear on the candidate's custom attributes, submit sample hiring manager feedback, and check that it logs to the timeline correctly.
Step 4: Launch Talent Rediscovery Against a Live Requisition
Open the job inside Skima AI, from the Jobs list or from within the job itself. Click the three-dot Actions menu and select Rediscover Candidates. A modal opens with options to narrow the scan: select a specific candidate segment, or leave it open to every synced Workable record.
Set the candidate created date range next: All time, Last 6 months, Last 1 year, or a custom window, and optionally check the location filter to restrict the scan geographically. Click Apply. A "Performing Rediscovery" notification appears while the scan works in the background, and turns green with a refresh option once it completes.
Step 5: Review, Filter, and Sync Feedback Back to Workable
Refresh the page and open the Rediscovered source tab inside View Candidates. Each match carries a Skima AI match score and reason bullets, tagged with a Rediscovery source label. Filter by Industry, Work Mode, Experience Level, Notice Period, Location, and Salary Range to narrow the list.
Once the shortlist looks right, generate a secure, no-login link and send it to the hiring manager. They click thumbs up, thumbs down, or maybe on each name, with an optional comment.
Furthermore, Skima AI writes that decision back into Workable as a timeline comment and, optionally, a dedicated custom attribute, keeping the feedback on the record without a second login.
What If Your Workable Database Is Messy or Duplicated
Workable does not support merging candidate profiles. It detects duplicates by matching email addresses, but only within a single job, not across the entire account. When a candidate applies to Job A and later to Job B, Workable creates two separate profiles linked to that same email, one for each job. An evaluation left on one profile never appears on the other.
A Candidate overview panel allows a recruiter to switch between those separate profiles. It does not combine them into one record. The only documented option for two profiles that clearly belong to the same person is to delete one manually after copying over any files or comments worth keeping through a note for the team. This is a one-way removal, not a merge, and any history on the deleted profile does not carry forward.
Skima AI treats this as ongoing maintenance rather than a one-time cleanup. It first matches on email, phone, or LinkedIn URL, then checks name, location, and company together as a backup signal.
When a match is found, whether through a batch upload or ATS sync, a field comparison modal displays differences across Name, Title, Company, Location, Email, Phone, and LinkedIn, tagging each mismatched field as Differs.
A recruiter chooses which values to keep and merges the records, combining history, notes, and tags, or adds the profile as a separate candidate if the two genuinely differ. Every resolved case is logged under the Duplicates tab, split into In Database, From Uploads, and Resolved sub-tabs for later audit.
5 Benefits of Candidate Rediscovery with Skima AI in Workable
Connecting Skima AI with Workable account's own application history gives 5 specific advantages, based on a documented 30-day pilot:
- Faster Time-to-Screen: Rediscovered candidates arrive already matched and scored against the open role, reducing time-to-screen by 58% in that pilot compared to starting a fresh search.
- Interviews Double From the Shortlist: The same pilot saw interviews booked from the top-10 shortlist double compared to the prior process.
- A Score That Shows Its Reasoning: Every rediscovered match carries a 0 to 100 score with reason bullets tied to specific skills and experience, replacing Resurface Candidates' qualitative labels with a number a recruiter can compare across candidates.
- One Timeline, No New Login: Match scores, reasons, and tags write to custom attributes and the candidate timeline a recruiter already checks inside Workable, with no separate dashboard to open.
- Manager Trust, Backed by Data: Hiring manager satisfaction rose 24 points on NPS in the same pilot, tied to a feedback link that logs every decision instead of a verbal note.
5 Best Practices to Rediscover Talent in Workable
Getting real ROI out of this connection takes a few habits beyond the integration itself. The 5 practices below cover search timing, data hygiene, and fitting rediscovery around a high-volume agency workflow:
- Start With Resurface Candidates: Use the native tool first to catch obvious matches without any setup. Once the one-month re-trigger limit becomes a bottleneck, or a numeric score matters for comparing candidates, move to a scoped Skima AI scan for the same requisition.
- Automate Rediscovery on Job Creation: Skima AI's setting under Preferences and General starts a database scan the instant a new job gets created inside the connected account. Enable it so no requisition goes live without a first look at existing applicant history.
- Connect Talent Pool Candidates to Open Roles With Reverse Search: Workable's Talent Pool stores speculative applicants and referrals without a job attached, useful for smaller teams. Reverse Search starts from one of those profiles and returns every open job they match, ranked by score.
- Segment Scans by Candidate Created Date: Choose a 6-month or 1-year window when a role calls for someone recently active. Widen to all time for a niche or hard-to-fill role where the applicant pool has stayed thin for years.
- Check the Duplicates Tab on a Fixed Schedule: Since Workable checks for duplicates by email on a per-job basis only, a candidate who reapplies with a different email or a slightly different name spelling never gets flagged natively. Reviewing the In Database sub-tab on a set schedule catches what Workable's own check does not.
Is Candidate Rediscovery Worth It for Your Recruitment Team?
Resurface Candidates uses the same Search with AI technology Workable sells for outside sourcing, and each search against a job counts toward the account's monthly Search with AI allowance.
A team filling three or four roles a year rarely reaches that limit. A team hiring continuously reaches it faster, and that changes how soon a continuously scored, unmetered option is worth the added cost.
Connecting Skima AI takes real coordination. Someone needs Super Admin access inside Workable to generate the API token, custom attributes configured with the right scopes, and a test cycle across two or three requisitions before anything touches a live role. That setup adds time and a subscription cost on top of a Workable plan a company already pays for.
A company with several years of closed jobs and the duplicate profiles that build up from Workable's per-job-only detection gives Skima AI more to work with than an account that started a few months ago.
However, a thin, recently built database returns a thin Rediscovered list no matter how the connection is set up, which makes the setup harder to justify for a newer account.
Pilot Candidate Rediscovery in Workable Now
For a company with a multi-year Workable history, a pilot with Skima AI can deliver a high ROI. Start with two or three live requisitions, ideally roles the company has filled more than once before, so there is a real historical pool to score against.
Connect Skima AI to Workable, configure the custom attributes, and test the connection on those two or three requisitions before going live.
Track how many rediscovered candidates reach an interview and how the hiring manager responds to the shortlist link. Skima AI's documented 30-day pilot with Workable showed a 58% drop in time-to-screen, interviews from the top-10 shortlist doubling, and hiring manager satisfaction rising 24 points on NPS. From there, expanding to more requisitions becomes a decision backed by the pilot's own numbers.
Frequently Asked Questions
1. Does Workable have a built-in talent rediscovery feature?
Partially, Workable's Resurface Candidates tool searches a company's own database and Talent Pool for a specific job, but a recruiter has to trigger it manually per job, and it will not search again for the same job within a month unless the requirements change substantially.
2. How do I rediscover candidates in Workable?
Start with Workable's native Resurface Candidates tool from the Find Candidates button on a job. For an ongoing, scored version, connect Skima AI through Workable's API, configure custom attributes for match score and reasons, then launch Talent Rediscovery on a live requisition.
3. What are the benefits of candidate rediscovery in Workable?
Based on a documented 30-day pilot, rediscovered candidates reduced time-to-screen by 58% and doubled interviews from the top-10 shortlist. Matches carry an explainable score and reason bullets, and hiring manager satisfaction rose 24 points on NPS.
4. Is Skima AI safe to integrate with Workable for talent rediscovery?
Yes, Skima AI scores candidates using resume-based evidence only, with no automatic rejections, and logs every hiring manager decision and shortlist action for audit. Data remains encrypted in transit and at rest, backed by a signed Data Processing Agreement.
5. Is candidate rediscovery useful for Workable's typical customers?
Yes, especially for smaller recruiting teams that rely on the Talent Pool to hold speculative applicants and referrals without a dedicated sourcing function. The value grows with the size of the closed-job history behind the account.
