A software company using Rippling closes a data analyst role after hiring someone else. The runner-up does not get flagged, scored, or grouped anywhere special. Their application just closes along with everyone else's.
Four months later, a similar analyst role opens on a different team. Nothing in Rippling checks that old application against the new one. A recruiter would have to remember that person by name and search for them directly, or the connection never happens at all.
This expert guide covers what tools Rippling provides for finding candidates already in the system. It also covers their limitations and how an AI integration addresses those to automate rediscovery, sourcing, and scoring past applicants.
What Is Candidate Rediscovery?
Candidate rediscovery involves sourcing and matching candidates already in your Rippling account to newly opened roles. This includes applicants from closed jobs, those in a talent pool, and individuals who progressed partially through a hiring process.
By automating checks against past candidates for new openings, your application history transforms into a valuable sourcing channel, eliminating the need for hiring teams to manually search old records.
Does Rippling Provide Talent Rediscovery?
No, Rippling doesn't provide a talent rediscovery feature or any way to source and rank past qualified applicants automatically. Its recruiting module includes candidate pools that a recruiter builds and names manually, and an Application Review tool that uses AI to screen incoming applicants against criteria that the recruiter sets for that specific job.
Additionally, a feature labeled "rediscover and re-engage" opens one of those manually built pools so a recruiter can decide who to contact again. It does not score or rank anyone.
Neither tool checks a closed job's full applicant database against a role that just opened, and neither operates without a recruiter starting the process. Application Review only evaluates people already applying to the job in front of it.
How to Rediscover Candidates in Rippling
Since Rippling has no built-in way to automatically source, score, or surface past applicants across open jobs, its users connect Skima AI to scan the database and rank matching profiles every time a role opens. Below is a 5-step workflow, from connecting your account to a finished shortlist:
Step 1: Connect Your Rippling Account With Skima AI
Create an API token scoped for employee read access, candidate application read access, job requisition read access, and custom field write access, using Bearer authentication over HTTPS. This connection uses Rippling's own REST API directly, with no middleware required.
- Read access: employee and candidate profiles, job applications, and job requisitions
- Write access: custom fields and candidate tags
- Pulled data: candidate contact details, resumes, application stage, and job requirements for scoring context
- Event triggers: application updates, requisition openings, and profile changes, delivered through Rippling's Workflow Studio
If workflow triggers are not configured, Skima AI falls back to safe polling every 10 to 15 minutes.
Step 2: Configure Custom Fields and Confirm the Connection
Define the custom fields Skima AI writes to, or authorize Skima AI to provision them during setup. These are a Skima AI match score, a number from 0 to 100, and AI Evidence, a detailed text field explaining the fit. A Skima Analyzed tag gets added at the candidate level, so screened applicants stay filterable inside Rippling.
Optional shortlist links and summaries get logged as candidate notes once a shortlist goes out.
Before rolling this out further, test the connection on a sample requisition. Confirm the Match Score and reasoning actually appear on that candidate's record before deploying it with role-based permissions.
Step 3: 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 lets you scope the scan two ways. Limit it to a saved candidate segment, or leave it open to every synced Rippling record.
Set the Candidate Created Date range next: All time, Last 6 months, Last 1 year, or a custom window, then click Apply. Skima AI scans the selected pool against this requisition'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 application history. 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, including people who never made it into a manually built talent pool but line up well against this requisition.
Step 5: Shortlist and Sync Feedback Back to Rippling
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 Rippling through the same API connection for the audit trail, logging it as a candidate note. The feedback stays visible on the record without anyone opening a second tool.
What If Your Rippling Database Is Messy, Duplicated, or Stale
Rippling lacks a duplicate-detection or merge workflow for candidate records inside Recruiting. When the same person applies to more than one role, or applies again months after being rejected, each application exists as its own record with no automatic check tying them together.
That gap grows as a company scales its hiring. A fast-growing company posting the same kinds of roles repeatedly accumulates duplicate applications every time someone reapplies with a slightly different email address or name spelling, and nothing catches it until a recruiter happens to notice.
Skima AI treats this as ongoing maintenance rather than a one-time project. It matches on email, phone, or LinkedIn URL first. It also checks matching name, location, and company together as a backup signal.
Furthermore, a field comparison modal appears before anything merges, flagging exactly what differs across the two records. Missing details get filled in through contextual profile enrichment instead of staying incomplete. A dedicated Duplicates tab logs every resolved case for later audit, split across In Database, From Uploads, and Resolved sub-tabs.
5 Benefits of Candidate Rediscovery with Skima AI in Rippling
Talent rediscovery with Skima AI changes what a Rippling team can do with its own application history in 5 direct ways:
- Faster Time-to-Screen: Rediscovered candidates arrive already scored, so recruiters skip the raw resume pile entirely. In a documented 30-day Rippling pilot, a team reduced 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.
- Explainable Match Scores With an Audit Trail: Every rediscovered match carries reason bullets tied to skills and experience, logged as a candidate note alongside a Skima Analyzed tag for instant filtering inside Rippling.
- Hiring Manager Engagement Without a New Login: The shortlist feedback loop keeps managers responding through a link, no Rippling credentials required. Manager satisfaction rose 24 points on NPS in that same pilot.
- Lower Cost Per Hire on Repeat Roles: Rediscovered candidates were already sourced once, so filling a role you've hired for before needs zero new job board spend.
5 Best Practices to Rediscover Talent in Rippling
None of these benefits happen automatically inside Rippling. The 5 practices below cover search timing, data hygiene, and fitting rediscovery around a growing company's hiring pace:
- Check Candidate Pools and Application Review First: Candidate pools and Application Review already cover people a recruiter has actively grouped or screened for a specific job. Use Skima AI's Talent Rediscovery to reach the much larger set of past applicants that neither tool checks again.
- Turn On Automatic Rediscovery for Every New Requisition: Skima AI's setting under Preferences and General starts a database scan the instant a new job is created. Enable it so no requisition slips through without a first look at your existing applicant history.
- Use Reverse Search for Employees Who Might Fit an Internal Opening: Since Rippling holds employee and candidate data in one place, Reverse Search can start from an employee profile and return every open role they match, ranked by score, surfacing internal moves nobody thought to check.
- Segment Scans With the Candidate Created Date Filter: Choose a 6-month or 1-year window when a role needs someone who was recently active. Widen to All time for a niche or hard-to-fill role where the applicant pool has always been thin.
- Review the Duplicates Tab as Hiring Volume Grows: A growing company posting similar roles across multiple teams accumulates duplicate applications quickly. Checking Skima AI's Duplicates tab on a regular schedule keeps that from turning into a backlog.
Is Candidate Rediscovery Worth It for Your Recruitment Team?
A company that has hired consistently through Rippling for a year or more builds up an application history that no manually built candidate pool fully captures. That backlog holds significant value. A candidate who didn't fit one opening often suits a similar role that comes up later, and nothing connects those two facts unless a recruiter searches for that person directly.
Setting up Skima AI takes some coordination on the technical side. Someone needs to create an API token with the correct scopes, map the custom fields Skima AI writes to, and test the connection on a sample requisition before going live. None of this is unusual for a Rippling integration.
For a small company with only a handful of open roles a year, that backlog stays thin and the setup effort is harder to justify. In contrast, for a fast-growing company hiring repeatedly across teams, the candidate history building up behind each closed role is exactly what rediscovery is built to put back to work.
Pilot Talent Rediscovery in Rippling Now
For growing companies hiring repeatedly on Rippling, a pilot with Skima AI can deliver a high ROI. Don't switch every open role over at once. Start with a sample requisition or two, ideally roles your company has filled more than once before.
Connect Skima AI, configure the custom fields, and launch Talent Rediscovery on those roles. Skima AI's own Rippling integration structures this as a 30-day pilot, with checkpoints along the way to track progress.
Track time-to-screen, how many rediscovered candidates reach an interview, and hiring manager response. Based on a documented 30-day Rippling pilot, expect around a 58% drop in time-to-screen and double the interviews from your top shortlist. From there, expanding to more requisitions becomes a decision backed by your own data.
Frequently Asked Questions
1. Does Rippling have a built-in talent rediscovery feature?
No, Rippling does not have a built-in talent rediscovery feature. It offers candidate pools that a recruiter builds manually and an Application Review tool that screens applicants against one job's criteria. A feature labeled "rediscover and re-engage" only reopens a manually built pool for review; it does not score anyone against a new role.
2. How to perform talent rediscovery in Rippling?
To perform talent rediscovery in Rippling, connect Skima AI to Rippling through an API token with the right scopes, configure custom fields, then launch Talent Rediscovery on a live requisition. Skima AI scans your application history, ranks candidates, and syncs hiring manager feedback back automatically.
3. What are the benefits of Rippling candidate rediscovery?
Rediscovered candidates arrive pre-scored, reducing screening time by over 58% in a documented 30-day pilot and doubling interviews from the top shortlist. Matches carry explainable reasons, a Skima Analyzed tag for filtering, and hiring manager feedback without a new login.
4. Is Skima AI safe to integrate with Rippling for talent rediscovery?
Yes, Skima AI encrypts candidate data in transit and at rest, follows SOC 2 Type II, ISO 27001, GDPR, and CCPA practices, and logs every AI decision and manager response for audit. Scores rely on resume-based evidence only, with no automatic rejections.
5. Is candidate rediscovery useful for Rippling's typical customers?
Yes, especially for growing companies hiring repeatedly across teams that refill similar roles over time. The value grows the more often the same kinds of positions, and sometimes the same candidates, come back into play.
