Manatal's AI Recommendation Engine can read a job description and rank the best-fitting people from the entire candidate database, including candidates who applied for a different role.
However, a recruiter still has to open the job and ask for those recommendations. Nothing inside Manatal checks the database automatically the moment a new requisition goes live, and nothing flags a strong match until a recruiter decides to look.
This expert guide covers what tools Manatal offers for finding candidates already in the database. 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 means matching people already stored in your Manatal database against a role that just opened. It covers applicants from closed jobs, candidates parked in a talent pool, and anyone who made it partway through a hiring process before the role was filled.
Your application history becomes a valuable sourcing channel once those past candidates are checked against new openings automatically. Most recruiting platforms let a hiring team pull up old records manually. Rediscovery performs that check every time a role opens, without anyone needing to remember to look.
Does Manatal Provide Candidate Rediscovery?
Yes, partially. Manatal's AI Recommendation Engine reads a job description and ranks candidates from the entire database. A related AI Advanced Search combines Boolean keyword search with semantic search.
Additionally, a Conversational AI Search allows a recruiter to ask plain-language questions about the entire candidate pool, including whether a specific person fits a specific role.
All three still require a recruiter to open a job and request the ranking or type a question. None of them automatically checks the database when a new requisition is created, and none sends an alert before a recruiter thinks to look.
How to Rediscover Candidates in Manatal
Manatal's native AI tools give you a manual head start. However, recruiting teams connect Skima AI to automatically source, match, and rank candidates in your Manatal database. Below is a 5-step workflow that starts with what Manatal already provides and then builds on it:
Step 1: The Native Path, Start With the AI Recommendation Engine
Open the job inside Manatal and look for the AI recommendation panel. The system reads the job description and returns a ranked list pulled from your entire candidate database within seconds, including people who applied to other roles.
This step gives you a real, ranked starting point, not just a filtered list. It only activates when a recruiter opens that job and asks for it, though, and it does not send a notification to flag that a strong match already exists.
For teams that want that automatic trigger, plus an audit trail hiring managers can act on without a new login, Skima AI builds on this next.
Step 2: Connect Your Manatal Account With Skima AI
Generate API tokens with read and write permissions scoped for candidate, job, and activity data in Manatal Settings. This connection uses Manatal's own REST API directly, with no middleware required.
- Read access: candidate profiles, job details, application status, and resume attachments
- Write access: activity notes and candidate tags
- Pulled data: candidate contact details, resumes, application history, and prior candidate movements for rediscovery context
- Webhook endpoints: candidate updates and application status changes
If webhooks are not configured, Skima AI falls back to safe polling every 10 to 15 minutes.
Step 3: Configure Tags and Confirm the Connection
Set up a Skima Screened tag and an optional Skima Shortlisted pipeline stage, or authorize Skima AI to provision them during setup. The Skima AI match score (number from 0 to 100), and reasons (explaining the fit), post as activity notes on the candidate record.
Optional hiring manager feedback and comments also log as activity notes once a shortlist goes out.
Before rolling this out further, upload resumes and trigger a few test workflows. Confirm the match scores, reasons, and feedback all write back correctly before applying it to live roles.
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 lets you scope the scan two ways. Limit it to a saved candidate segment, or leave it open to every synced Manatal 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 5: Review, Filter, and Sync Feedback Back to Manatal
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.
Once the shortlist looks right, generate a secure, no-login link and send it to the hiring manager. They can click thumbs up, thumbs down, or maybe on each candidate, with an optional comment. Skima AI writes that decision back into Manatal as an activity note, so the feedback stays on the record without anyone switching to a second tool.
What If Your Manatal Database Is Messy or Duplicated
Manatal features a Duplicate Management System. Admins select which fields count as a match, including full name, email address, or phone number. They can also enable auto-merging for specific sources like a career page or job board, allowing new duplicates from those channels to merge automatically without manual review.
Moreover, a separate Duplicates view displays every potential match across the database. Each candidate profile includes a one-click Check for Duplicate option.
This system merges records only when a new duplicate enters through a covered source or when someone accesses the database-wide Duplicates view. Additionally, it addresses a different issue than rediscovery: determining that two records belong to the same person differs from assessing whether a new, qualified candidate from a closed role fits the one that just opened.
Skima AI treats this as ongoing maintenance rather than a one-time project. It first matches on email, phone, or LinkedIn URL, then checks name, location, and company as backup signals. A comparison view highlights differences between two records before merging, fills in missing details through enrichment, and logs every resolved case in a Duplicates tab for auditing.
5 Benefits of Candidate Rediscovery with Skima AI in Manatal
Connecting Skima AI with Manatal's existing AI tools gives 5 specific advantages a recruiter can act on immediately:
- Faster Time-to-Screen: Rediscovered candidates arrive already scored, so recruiters skip the raw resume pile entirely. In a documented 30-day Manatal 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 an activity note alongside a Skima Screened tag for instant filtering inside Manatal.
- Hiring Manager Engagement Without a New Login: The shortlist feedback loop keeps managers responding through a link, no Manatal 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 Manatal
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 the Recommendation Engine: Manatal's own recommendation panel already covers one job at a time when a recruiter remembers to open it. Use Skima AI's automatic scan to catch every other open role and every closed requisition nobody thought to check.
- Automate Rediscovery for New Requisitions: 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 Low-Ranked Candidates: A candidate who ranked poorly in Manatal's recommendations for one job may fit a different open role well. Reverse Search starts from that profile and returns every open job they match, ranked by score.
- Segment Scans by Candidate Date: 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.
- Pair Duplicates Tab With Auto-Merge Settings: Manatal's auto-merging only covers the sources an admin configured. Reviewing Skima AI's Duplicates tab on a set schedule catches records from every other channel that Manatal's own settings were never told to watch.
Is Candidate Rediscovery Worth It for Your Recruitment Team?
An agency or in-house recruitment team that has used Manatal across many roles for a year or more builds up a candidate database. Manatal's own AI Recommendation Engine only searches one job at a time.
That backlog holds significant value. A candidate who ranked well for one role but did not get it often fits a similar opening that comes up later, and nothing connects those two facts unless a recruiter opens that specific job and asks.
For a small team with only a few open roles a year, that backlog stays thin and the setup effort is harder to justify. In contrast, for a high-volume agency or a growing in-house team, the candidate history spread across dozens of past requisitions is exactly what rediscovery is built to put back to work.
Pilot Candidate Rediscovery in Manatal Now
For high-volume recruiters and agencies hiring repeatedly on Manatal, a pilot with Skima AI can deliver a high ROI. Start with a few key requisitions, ideally roles your team has filled more than once before.
Connect Skima AI, configure the Skima Screened tag, and launch Talent Rediscovery on those roles. Skima AI's own Manatal integration structures this as a 30-day pilot, with dashboard reports and alerts to track ROI along the way.
Track time-to-screen, how many rediscovered candidates reach an interview, and hiring manager response. Based on a documented 30-day Manatal 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 Manatal have a built-in candidate rediscovery feature?
Partially, Manatal's AI Recommendation Engine ranks candidates from the entire database against a job description. It only activates when a recruiter opens that specific job and asks for recommendations, rather than checking automatically the moment a new requisition is created.
2. How to conduct talent rediscovery in Manatal?
To conduct talent rediscovery in Manatal, connect Skima AI to Manatal through API tokens with read and write permissions, configure the Skima Screened tag, 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 Manatal 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 Screened tag for filtering, and hiring manager feedback without a new login.
4. Is Skima AI safe to integrate with Manatal for talent rediscovery?
Yes, Skima AI encrypts all API communication with TLS, uses token-based authentication and role-based access control, and logs every AI decision and manager response as an activity note for audit. Scores rely on resume-based evidence only, with no automatic rejections.
5. Is candidate rediscovery useful for Manatal's typical customers?
Yes, especially for recruitment agencies and high-volume in-house teams that refill similar roles across many clients or departments. The value grows the more often the same kinds of positions, and sometimes the same candidates, come back into play.
