Open Roles Close Themselves
Applicants are screened against the job requirements; the loop then proposes acknowledgments, phone-screen invites and rejections, each behind its own setting, and alerts the recruiter.
See Recruiting Agent In Action
See how every applicant is screened and scored the moment they apply — with ranked shortlists, drafted outreach and offer letter text for review, job-related scoring weights, identifier masking, and a record of what the agent proposed and who decided.
HOW IT WORKS
How it closes an open role
From a new application to the next candidate email — using the same job requirements, scoring weights, and approval chain you already set. It works the funnel before recruiters have to.
1. Detect
A new application has had no acknowledgment, a screening recommends advancing or declining a candidate, or screened candidates are waiting on a recruiter. The loop checks every 15 minutes, each check behind its own setting.
2. Decide
Each applicant is screened against the actual job requirements, scored on the factor weights you set. The loop acts on that screening's recommendation.
3. Act
Proposes an application-received email, a phone-screen invite to a candidate in Screening whose screening recommends advancing or a rejection email (the candidate is marked Rejected), and alerts the job's recruiter when screened candidates are waiting. Acknowledgments, phone-screen invites, rejections and recruiter alerts are each behind their own setting; invites, rejections and alerts are off until you switch them on.
4. Log
Every proposal and what happened to it is recorded in the agent's activity log. Every screening is saved with its scores and reasoning, and every agent tool call is logged with who asked and which tool ran.
AUTONOMY YOU CONTROL
Three levels of autonomy. You pick.
Start with it off — it takes no action until you say so. Move to approve for a one-tap checkpoint on every action. Let it run on its own when you're ready.
Off — manual only
The loop proposes nothing on its own. Screening still scores applicants, and a recruiter decides what to do with each result.
Approve
Every proposed email or recruiter alert waits for a one-tap decision from a recruiter. The pending queue is your daily standup.
Auto
Application acknowledgments and recruiter alerts go out on their own once the agent has earned your trust. Rejections and phone-screen invites always come back to you for approval.
Every screening is on the record, with its score and reasoning
Each candidate the agent screened has a screening record with the match score, the recommendation, and the reasoning behind it. Recruiters open the screening list or the ranking for a job to see who was scored and how.
- Match score and recommendation on every screening the agent ran.
- Ranking per job — candidates for a role ordered by their screening score.
- Factor scores on every screening — technical fit, experience, education, and cultural fit, with strengths and gaps.
One console — the recruiting team's home for autopilot operations
The AI Recruiter console is the buyer-facing landing for recruiters and TA leaders. Open-roles count sits front and center with a sparkline trend. The "What the AI handled" feed shows the most recent actions. The "AI actions to approve" list surfaces the candidate emails the agent proposes, waiting for a decision. The autonomy dial is right there.
- Hero metric + trend — open roles with a 30-day trend line, and the number filled in the last 30 days beside it.
- "What the AI handled" feed — the most recent acknowledgments, phone-screen invites, rejections and recruiter alerts, each marked Done by AI or AI couldn't complete.
- "AI actions to approve" list — the acknowledgment, phone-screen invite and rejection emails (and recruiter alerts, until the agent has earned trust) it proposes, approved or declined inline.
- Autonomy dial — Off, Approve or Auto, set on the console.
Why Hiring Slows Down — And Where It Breaks
AI Recruiter attacks the specific places the hiring funnel stalls — not generic "AI for recruiting," but tools mapped to where the day actually leaks time.
Too Many Candidates, Not Enough Screening Hours
A high-traffic req can attract hundreds of applicants. Manual résumé scans don't scale, and recruiters end up rejecting good candidates because they ran out of time before they ran out of pipeline.
Pipeline Data Scattered Across Tools
The ATS has stages, email has the conversation, chat has the hiring manager debrief, and a spreadsheet has the comp band. Nobody has the one view, and "what's the status on Priya?" turns into a 20-minute thread.
Recruiters Retyping The Same Outreach For Every Candidate
Initial reply, follow-up, interview invite, rejection, offer follow-up — five templates slightly personalized for every candidate, multiplied by every open req. The work is necessary; doing it by hand isn't.
Hiring Managers And Recruiters Seeing Different Statuses
The recruiter moves a candidate to "interview" in the ATS; the hiring manager hears "still screening." Mismatched views breed mistrust between recruiters and the teams they hire for, and pipeline reviews get spent reconciling instead of deciding.
Interview Feedback Arrives Days Late, In A Format Nobody Can Compare
Three interviewers, three free-text email replies, one panel debrief that gets postponed twice. By the time feedback is consolidated, the candidate has accepted somewhere else or the team is comparing apples to oranges because every interviewer answered different questions.
Offers Get Drafted From Memory And Land With The Wrong Numbers
The recruiter pulls the comp band from a slack thread, the equity grid from a spreadsheet, and the start date from the hiring manager's last email. One detail slips, the offer letter goes out wrong, and the redo costs a day plus the candidate's confidence in how organized the company is.
AI Recruiter At A Glance
AI Recruiting
Pipeline, screening and interviews through offer drafts.
Inside AI Recruiter — The Actual Capabilities
Every block below maps to a real tool the agent uses against your Recruiting app data. Permissions and configuration are preserved on every call — read or write — and each tool call is logged with the requesting user.
See The Whole Pipeline Without Switching Apps
Ask in plain English — the agent pulls live data from the Recruiting app, respecting the same role-based permissions admins already configured. No exports, no copy-paste between tools, no separate "AI dashboard" to keep in sync.
- List open jobs by status, department, or location — answers "what's open right now?" without opening a dashboard.
- View candidates for a posting or stage — applied, screening, interview, offer, or hired.
- Pull a candidate's full record — resume, recruiter notes, current status, and interview summary in one response.
- Get pipeline counts by stage for any job or across all active jobs.
AI Screening, Grounded In The Job You're Hiring For
The agent scores candidates against the actual job requirements stored in MangoApps — not a generic résumé parse. Scores are persisted to the candidate record so recruiters and hiring managers see the same number, and the signals behind each score are explainable on request.
- Score a single candidate against a specific job, with the fit signals the model used.
- Rank every candidate on a posting in one shot — sorted by fit score, ready to triage.
- Re-read existing scores instead of re-running the model — keeps numbers stable and saves cost.
- Job-related scoring — screening scores are weighted on the factors you set — technical fit, experience, education, and cultural fit.
Schedule Interviews Without Leaving The Chat
Ask "what interviews do I have this week?" or "schedule Priya for Tuesday at 2 with the panel" — the agent books it once you confirm, emails the candidate and notifies the interviewers. It does not check anyone's availability, so you name the time.
- List upcoming interviews for a specific job, or your own.
- Schedule a new interview — date, time, type (phone, video, onsite, panel), and interviewers — in one prompt.
- Confirmation prompt before booking — the agent never schedules silently.
- Every action logged — each tool call records the requesting user and which tool ran (AI request detail is kept 30 days).
Draft Outreach And Move Candidates — With A Human In The Loop
The agent drafts personalized emails and moves candidates between stages, but every write goes through an explicit approval step. Recruiters keep the decision; the agent removes the typing and the context-switching.
- Generate outreach drafts — initial response, follow-up, rejection, interview invite, or offer follow-up — personalized to the candidate and the job.
- Update candidate status with optional notes — screening, interview, offer, hired, or rejected.
- Confirmation before a record changes — outreach sends and status changes ask for confirmation first.
- Identifier masking on what you type — SSNs and card, bank-account, license, and passport numbers you type are masked before the agent's model call.
Offer Letters Drafted With Benchmark Estimates On Hand
When it's time to make an offer, the agent drafts the offer letter text for review and returns a built-in compensation estimate for the role and experience level. The offer record and its approval chain live in Offer Manager — nothing is auto-sent.
- Draft offer letter text with base salary, signing bonus, and start date — for a person to review, never auto-sent.
- Compensation estimates for any role by experience level — entry through executive. Built-in estimates, not a live market feed.
- Hands off cleanly to Offer Manager Agent for full negotiation tracking, approval workflows, and acceptance.
- Approval gates respected — offers are submitted and approved in Offer Manager, through the chain HR already configured.
Outcomes Teams Can Measure
The agent is built to compress hiring cycle time and free recruiter hours from work that shouldn't have been manual. Measure against your current baseline so you can show the hiring team where the agent is helping — and where it still isn't.
- Time to fill — days from req open to offer accepted, across the full pipeline.
- Recruiter hours per req — how much of the screening + outreach + scheduling work the agent absorbs.
- AI screening throughput — candidates scored per hour vs manual review baseline.
- Recruiter / hiring-manager status alignment — share of candidates where the recruiter view and hiring-manager view agree.
- AI screening override rate — how often recruiters disagree with the AI rank, an early-warning signal for model drift or job-description issues.
Write Actions, Always With A Human In The Loop
Every action that changes a candidate record, books an interview, drafts an offer or sends outreach is gated by explicit confirmation and runs as the requesting user. The agent never sends outreach, changes a candidate's stage, or generates an offer letter silently.
- Confirmation-gated writes — moving a candidate's stage, updating their contact details, scheduling an interview, drafting an offer letter, drafting or sending outreach, and staging an import all require explicit confirmation.
- Job-related scoring factors — screening scores are weighted on technical fit, experience, education, and cultural fit, at the weights you set.
- Identifier masking — SSNs and card, bank-account, license, and passport numbers you type are masked before the agent's model call.
- Every tool call logged — read or write, the log records the requesting user and which tool ran (AI request detail is kept 30 days).
WHAT TEAMS TRY INSTEAD
The four alternatives — and why none of them combine live pipeline, weighted job-related scoring, and human-gated writes
Recruiting AI is a crowded space with serious adverse-impact risk. The honest gap is that most options either rank candidates against criteria nobody set, draft outreach without a human approval step, or read from a separate ATS that drifts from the hiring team's actual notes.
Pasting resumes into ChatGPT, Claude, or Copilot
General-purpose AI scoring resumes with no set criteria
- Reads live pipeline records — no resume PII pasted into a public chat window
- Screening scores are weighted on the job-related factors you set — technical fit, experience, education, and cultural fit
- SSNs and card, bank-account, license, and passport numbers you type are masked before the agent's model call
Greenhouse AI / LinkedIn Recruiter AI / Workday Recruiting Assistant / Eightfold AI
Vendor-trapped recruiting AI inside one ATS or sourcing tool
- Reads from the platform's own pipeline records — no synced shadow copy that drifts from the hiring team's notes
- Lives where hiring managers already are — no separate ATS or sourcing seat required to draft outreach
- Reaches across the rest of the platform — manager notes, training plans, comp ranges — not just a single ATS module
Custom AI scoring scripts and outreach automations
A talent-ops team's quarterly project with no activity log
- Weighted scoring and identifier masking shipped — no engineering quarter to retrofit
- Writes are confirmation-gated (status, offer, outreach) — no automation accidentally moving a candidate
- Every tool call is logged with the requesting user and which tool ran
The manual fallback — recruiters skim, hiring managers wait
A funnel that loses good candidates to delay and inconsistency
- Live pipeline visibility — recruiters and hiring managers see the same stage at the same time
- Outreach drafts assemble in plain English with the right tone and the right req context — recruiter confirms and sends
- Status changes happen in chat with confirmation, not a five-tab ATS tour
PLATFORM ADVANTAGE
AI Recruiter inherits everything the app already enforces
A standalone recruiting AI has to plumb identity, scoring criteria, identifier masking, and logging. AI Recruiter gets all of it for free.
Confirmation-gated writes
Stage moves, contact-detail updates, interview bookings, offer drafts, outreach and imports all require explicit confirmation. The model proposes; the recruiter commits.
Job-related scoring factors
Screening scores are weighted on the job-related factors you set — technical fit, experience, education, and cultural fit.
Identifier masking
SSNs and card, bank-account, license, and passport numbers you type are masked before the agent's model call. Candidate emails and phone numbers stay readable so the agent can update records and draft outreach.
Live pipeline
Reads the same Recruiting app records the rest of the team works from. No synced shadow copy that drifts from the hiring team's notes.
Every tool call logged
Every read and every write logs the requesting user and which tool ran. AI request detail is kept 30 days.
Permission-aware
Recruiter, hiring manager, and coordinator roles each see the slice they are entitled to. Comp ranges and offer details follow the existing access model.
INDUSTRY FIT
Industries where recruiting AI has to be careful
AI Recruiter earns its keep where hiring volume is high, the adverse-impact bar is real, and the talent team is leaner than the funnel.
Retail
High-volume seasonal hiring with consistent screening and confirmation-gated outreach — no two recruiters scoring the same role differently.
Healthcare
Clinical hiring scored on the same job-related factors for every candidate, in one pipeline.
Manufacturing
Plant-floor hiring at scale — drafts and outreach go through the same approval step regardless of who in talent-ops is on call that day.
Financial Services
Every screening keeps its factor scores and reasoning, and every agent tool call is logged with the requesting user.
Hospitality
Property-level hiring at scale — district recruiters work from one pipeline with the same scoring weights regardless of property.
Public Sector
Every agent tool call is logged with the requesting user, and every screening keeps its scores and reasoning on the record.
WHY MANGOAPPS WINS
An embedded recruiting agent beats a horizontal AI, an ATS-vendor module, or a custom build on every axis
The argument talent, legal, hiring managers, and finance all share — and the one Greenhouse or Workday structurally cannot answer.
Cheaper than the alternatives
No Greenhouse AI tier, no LinkedIn Recruiter AI seat, no Eightfold subscription, no engineering team maintaining a custom scorer.
More secure
Typed identifiers masked, record changes confirmation-gated, every tool call logged. AI requests go to the platform's configured model providers, like every MangoApps agent.
Easier to deploy
Already deployed if Recruiting is on. Agent picks up the live pipeline, comp ranges, and approval flows the same day.
Easier to use
Hiring managers stay in the same workforce app instead of a separate ATS. Status visibility and outreach drafts happen in chat.
Easier to manage
One set of scoring weights, one identifier-masking layer, one place to review the agent's activity — every recruiter works to the same settings.
Easier to extend
Shares the agentic-tool framework with every other MangoApps agent. New recruiting tools (a new screening signal, a new pipeline view) ship as tools.
AI is actually better
A horizontal AI can rank resumes. AI Recruiter ranks on the weights you set, routes status changes through human confirmation, and logs every tool call.
At our head office within HR, marketing, and finance, we use ChatGPT quite a bit, and MangoApps AI Helper is a useful tool for us. As I go around to the stores, I’ve been showing them how the AI Intranet Helper can find things and answer plain language questions about our resources, and they’re quite excited about that.
Alison van Zijl
Head of Human Resources
Full House
Customer Success
MangoApps Customers
Organizations that trust MangoApps to run every workflow across their workforce.
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Frequently Asked Questions About AI Recruiter
Work across the hiring pipeline — list jobs, view candidates, get full candidate details, pipeline counts, AI screening + ranking with persisted scores, upcoming interviews, schedule interviews, draft offer letters, draft or send candidate outreach, move candidates between stages, update candidate contact details, stage candidate imports, and analyze compensation against built-in estimates. With autonomy on, it also proposes application acknowledgments, phone-screen invites and rejections, and alerts a job's recruiter about screened candidates who are waiting.
Yes — but every change asks for confirmation first: moving a candidate's stage, updating their contact details, scheduling an interview, drafting an offer letter, drafting or sending outreach, and staging an import. Screening a candidate saves the AI score it produces; it does not change the candidate's stage. In autonomous mode, phone-screen invites and rejections always wait in the approval queue until a person decides; acknowledgment emails and recruiter alerts can go out on their own once the agent has earned trust. Everything else the agent does is read-only.
AI Recruiter scores each candidate on the job-related factors you weight — technical fit, experience, education, and cultural fit. Each screening returns the four factor scores with its strengths, gaps, and reasoning, so a recruiter can check it against their own judgment.
Time to fill (open-to-accepted), recruiter hours per req, AI screening throughput vs manual baseline, recruiter / hiring-manager status alignment, and AI screening override rate. Treat these as directional against your pre-agent baseline.
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