An operations coordinator has logged strong performance reviews and finished two skills certifications inside isolved over the past two years. A team lead opening posts in a different department the same month her manager mentions she is ready for more responsibility.
Nothing in isolved's recruiting module connects those two facts, even though the performance data, the certifications, and the open requisition all exist on the same platform.
In this guide, we cover what isolved offers for finding candidates already inside its system, whether that means past applicants or existing employees, and how connecting an external AI tool closes the gap with a scored, automated search.
What Is Candidate Rediscovery?
Candidate rediscovery involves searching stored candidates in isolved's HCM for newly opened roles. This includes applicants from closed positions, those in a talent database, and candidates who partially completed the hiring process.
With a year or two of application history, these stored candidates become an active sourcing channel, as rediscovery checks them automatically against each new opening, eliminating the need for recruiters to remember names.
Does isolved Provide Candidate Rediscovery?
No, isolved does not offer a candidate rediscovery feature nor any way to automatically source and rank past applicants for a newly opened role. Its Talent Acquisition suite includes a candidate database for storing resumes, prioritized candidate scoring for individuals who have already applied to a specific job, and Talent Scout.
Talent Scout is a conversational AI agent introduced in September 2025 that recommends candidates through natural language. It primarily focuses on reaching Indeed's external candidate pool rather than resurfacing a company's own closed-job applicants.
None of these tools scans the applicant history of a closed requisition against a newly opened role. Candidate scoring evaluates only those already in a specific job's pipeline. Moreover, Talent Scout relies on a recruiter to describe what they want in each new search.
For a company that hires similar roles repeatedly, this gap means that qualified past applicants, as well as current employees with the right skills on file, remain unchecked unless someone remembers to look.
How to Rediscover Candidates in isolved
Since isolved has no native way to scan its own applicant and employee history against a new opening, Recruitment teams connect Skima AI to scan both past applicants and current employees, using the performance and skills data isolved already stored.
Below is a 5-step workflow, from connecting the account to a finished shortlist:
Step 1: Connect Your isolved Account With Skima AI
Create an isolved API user with read and write permissions on Employee, Requisition, Application, and custom segment data, then set up OAuth 2.0 or API-key authentication scoped for the HCM, Recruiting, and Talent modules. This connection uses isolved's own REST API directly, with no middleware required.
- Read access: employee profiles (tenure, performance ratings, skills, certifications), candidate and application data, requisition details, and resume text
- Write access: job application custom fields, candidate tags, and employee notes
- Pulled data: contact details, resumes, application status, requisition requirements, and employee performance and skills data for internal matching
- Event triggers: new application submissions, candidate status updates, requisition postings, employee profile changes, and skills assessment updates, delivered through isolved's webhooks
Step 2: Configure Custom Fields and Confirm the Connection
Define the job application custom fields Skima AI writes to, match score and reasons, and map any custom segments isolved uses. Add the SKIMA Screened tag at the candidate level so screened applicants stay filterable. If webhooks are not configured, Skima AI falls back to scheduled polling instead.
Before rolling this out further, test the connection with a sample requisition and application. Confirm the Match Score and Reasons appear, verify an internal employee match surfaces correctly if one exists, and check that hiring manager feedback logs correctly before applying it to live roles.
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 opens with options to narrow the scan: select a specific candidate segment, or leave it open to every synced isolved 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 4: Review and Filter the Rediscovered Matches
Open the View Candidates tab to see a ranked list pulled from across the applicant and employee 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 internal employees whose performance and skills data line up with the requisition but never applied for it.
Step 5: Shortlist and Sync Feedback Back to isolved
Build the shortlist from the ranked matches Skima AI surfaced. Generate a secure, no-login link and send it to the hiring manager. They click thumbs up, thumbs down, or maybe on each candidate, with an optional comment.
Skima AI writes that decision back into isolved through the same API connection, logging it against the candidate's application record or, for an internal match, as an employee note. The feedback stays visible on the record without anyone opening a second tool.
What If Your isolved Database Is Messy or Duplicated
isolved's Talent Acquisition suite depends on a candidate database that stores and arranges applicant resumes. However, it lacks a feature to detect or merge records for the same person, even if they applied using a different email or slight name variation. Recruiters often only notice this overlap when they recognise a name during application reviews.
This problem escalates for companies that have used isolved for years across various departments, as each new application generates an additional untracked record instead of updating an existing one.
In comparison, Skima AI treats this as ongoing maintenance. It first matches email, phone, or LinkedIn URL, and then cross-references name, location, and company as backup. When a match is detected, a modal shows differences in key fields, allowing recruiters to merge or create new records as needed. Resolved cases are logged under the Duplicates tab for auditing.
5 Benefits of Candidate Rediscovery with Skima AI in isolved
A documented 30-day pilot with isolved reported 5 specific highlights once Skima AI starts scoring a company's stored candidate and employee history:
- Faster Time-to-Screen: Rediscovered candidates arrive already matched and scored against the open role, reducing time-to-screen by 58% compared to starting a fresh search.
- Interviews Double From the Shortlist: The same pilot saw interviews booked from the shortlist double compared to the prior process.
- Employees Included, Not Just Applicants: Scoring draws on performance ratings, tenure, and skills data isolved already stored, so an internal employee ready for a new role surfaces alongside external candidates.
- One Record, No New Login: Match scores, reasons, and tags write directly to the same application fields and notes a recruiter already checks inside isolved.
- Manager Feedback That Sticks: 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 isolved
Getting high ROI out of this connection takes a few habits beyond the setup itself. The 5 practices below cover search timing, data hygiene, and fitting rediscovery around a full HCM platform:
- 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 history.
- Include Internal Employees in Every Scan: Since isolved already stores performance ratings and skills data, leave internal employee matching turned on for every requisition instead of treating it as a separate search.
- Extend Reverse Search to Skills and Certifications: A candidate or employee who did not fit one role can still match a different opening based on a certification or skill on file. Reverse Search starts from that profile 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.
- Review the Duplicates Tab on a Set Schedule: Since isolved has no native way to catch a repeat applicant under a different email or name spelling, checking the In Database sub-tab regularly is the only way to keep this from piling up.
Is Candidate Rediscovery Worth It for Your Recruitment Team?
isolved's candidate database stores resumes but does not check them against new openings, offering no preview of a company's history. The value of that history largely depends on the duration the company has used isolved and the number of roles filled.
A company using isolved as its full HCM provides Skima AI with more data than one relying solely on applicant tracking. Performance ratings, tenure, and skills data serve as matching signals for internal roles. Conversely, a newer account or one with few closed requisitions yields a thinner Rediscovered list, complicating early setup justification.
Pilot Candidate Rediscovery in isolved Now
For a company with a multi-year history with isolved, a pilot using Skima AI can deliver a high ROI. Start with two or three live requisitions, ideally roles the company has filled multiple times before. This way, there is a real historical pool to score against.
Connect Skima AI to isolved, configure the custom fields, and test the connection on a sample requisition and application before going live. Skima AI's integration with isolved structures the rollout in phases: first, a pilot on a few requisitions, then a wider rollout across business units with stakeholder training and metrics tracking.
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 isolved showed a 58% drop in time-to-screen.
Additionally, interviews from the shortlist doubled, and hiring manager satisfaction rose by 24 points on NPS. From there, expanding to more requisitions and business units becomes a decision supported by the pilot's own numbers.
Frequently Asked Questions
1. Does isolved have a built-in talent rediscovery feature?
No, isolved's Talent Acquisition suite offers a candidate database, scoring for a specific job's applicants, and Talent Scout for natural-language sourcing, but none of these check a closed requisition's applicant history against a role that just opened.
2. How do I rediscover candidates in isolved?
Connect Skima AI to isolved through its API, configure custom fields for Match Score and Reasons, then launch Talent Rediscovery on a live requisition. Skima AI scans both past applicants and internal employees, ranks them, and syncs hiring manager feedback back automatically.
3. What are the benefits of candidate rediscovery in isolved?
Based on a documented 30-day pilot, rediscovered candidates reduced time-to-screen by 58% and doubled interviews from the top-10 shortlist. Matches include internal employees using performance and skills data, and hiring manager satisfaction rose 24 points on NPS.
4. Is Skima AI safe to integrate with isolved for talent rediscovery?
Yes, Skima AI scores candidates using resume-based and skills-based evidence only, with no automatic rejections, and logs every hiring manager decision and shortlist action for audit. Data stays encrypted in transit and at rest, in line with SOC 2 and GDPR practices.
5. Is candidate rediscovery useful for isolved's typical customers?
Yes, especially for companies using isolved as a full HCM platform, where performance ratings, tenure, and skills data already exist and can surface internal candidates alongside past applicants. The value grows with the size of that combined history.
