Rolebot Integration for Greenhouse: Automate Passive Candidate Sourcing and Outreach
Titus Juenemann •
August 28, 2024
TL;DR
This guide explains the Rolebot integration for Greenhouse: how it automates passive candidate sourcing using AI, links Greenhouse jobs to Rolebot, exports accepted candidates automatically, and runs outreach to convert passive prospects to interviews. It covers features, who benefits, implementation steps, KPI tracking, troubleshooting, ROI examples, and security considerations. The conclusion: integrating Rolebot with Greenhouse reduces manual sourcing workload, speeds time-to-first-interview, and centralizes candidate data — and pairing the pipeline with an AI resume screener like ZYTHR further reduces screening time while improving accuracy.
Rolebot automates passive recruiting by sourcing, qualifying, and initiating outreach to top talent and sending candidates directly into Greenhouse. Plug in two exemplar profiles and Rolebot uses AI and signals from 100+ public data points to deliver up to 15 look‑alike candidates per role per day, then lets your team thumbs up/down the matches. Once you accept a candidate in Rolebot, their profile — including email, phone, and social links — is exported automatically to the linked Greenhouse job. Rolebot also handles outreach and scheduling to convert selected passive candidates to interviews, and provides reporting metrics useful for measuring time-to-hire and candidate quality.
At a high level, the Rolebot–Greenhouse integration removes manual sourcing and repetitive data entry while maintaining visibility in your ATS. It’s designed to sit alongside recruiter workflows in Greenhouse: launch roles into Rolebot directly from a job form, review daily rounds of AI-sourced candidates, and export accepted profiles into Greenhouse as Candidates.
Core features enabled by the integration
- Automated daily sourcing Rolebot delivers up to 15 AI-curated look‑alike candidates per role per day based on two seed profiles.
- One-click export to Greenhouse Accepted candidates are automatically created in the linked Greenhouse job with contact details and source metadata.
- Outreach and interview lock-down Rolebot runs initial outreach campaigns and coordinates first-interview booking for selected prospects.
- Feedback-driven improvement Thumbs up/down feedback is used by Rolebot’s proprietary algorithms to refine match quality continuously.
- Operational transparency Linked candidates display a Greenhouse badge in Rolebot and can be searched in your Greenhouse Candidates + Prospects database.
- Regional and company-scale support Designed for global deployments (North America, EMEA, APAC, South America) and scales from mid-market to enterprise.
AI resume screener for Greenhouse
ZYTHR scores every applicant automatically and surfaces the strongest candidates based on your criteria.
- Automatically screens every inbound applicant.
- See clear scores and reasons for each candidate.
- Supports recruiter judgment instead of replacing it.
- Creates a shortlist so teams spend time where it matters.
| Name | Score | Stage |
|---|---|---|
| Oliver Elderberry |
9
|
Recruiter Screen |
| Isabella Honeydew |
8
|
Recruiter Screen |
| Cher Cherry |
7
|
Recruiter Screen |
| Sophia Date |
4
|
Not a fit |
| Emma Banana |
3
|
Not a fit |
| Liam Plum |
2
|
Not a fit |
Who benefits most from Rolebot + Greenhouse
- High-volume technical hiring teams Teams hiring for many similar roles can accelerate pipeline generation without adding sourcers.
- Recruiters with bandwidth limits When recruiter availability is the bottleneck, Rolebot sources and seeds candidates to reduce time spent sourcing.
- Talent acquisition teams measuring time-to-hire Automated outreach and export into Greenhouse shortens candidate handoff and reduces time-to-first-interview.
- Global or distributed hiring programs Support for multiple regions and Greenhouse job linkage simplifies multi-market rollouts.
- Teams focused on consistent candidate data Automatic export ensures contact details and social links transfer cleanly into Greenhouse, reducing manual entry errors.
Quick comparison: Manual sourcing vs Rolebot + Greenhouse
| Aspect | Typical outcome |
|---|---|
| Daily candidate volume | Manual: 0–5 curated prospects; Rolebot: up to 15 look‑alikes/day per role |
| Time spent per candidate | Manual: 10–30+ minutes; Rolebot: <10 minutes to review and accept/reject |
| Data entry into ATS | Manual: manual creation and updates; Rolebot: automatic export on accept |
| Outreach | Manual: recruiter or sourcer executes outreach; Rolebot: automated initial outreach and interview scheduling |
How to launch a Greenhouse job into Rolebot: open the pop-up window in Rolebot, select the Greenhouse tab, and choose the job by its Req ID from the 'Launch a new role' field. Assign user emails for access and click 'Launch Role' — the role appears in Rolebot and daily matching begins. This process centralizes role definitions and ensures exported candidates map to the correct Greenhouse job.
Candidate review and handoff workflow
- Review round Rolebot presents candidate rounds daily; reviewers give a thumbs up/down/skip in under 10 minutes.
- Accept = export Accepted candidates are created automatically in Greenhouse with all available contact and profile fields.
- Rejected candidates Rejected profiles are retained in Rolebot for algorithmic learning and future refinement; they are not exported.
- Search and reconcile Click the GH logo next to a Rolebot candidate to search matching prospects in Greenhouse and avoid duplicates.
Key metrics to track after integration
| Metric | Practical target / why it matters |
|---|---|
| Time-to-first-interview | Target: reduce by 20–40% — measures speed of converting passive candidates to interviews |
| Candidates accepted per role | Target: consistent increase in quality matches — indicates how well seed profiles are tuned |
| Sourcing hours saved | Target: estimate proportional recruiter hours reclaimed — shows operational efficiency |
| Export accuracy (data completeness) | Target: >95% of accepted candidates export with email/phone populated — reduces follow-up work |
Common questions about the integration
Q: Does Rolebot require a partner implementation fee?
A: No. The integration does not require a partner implementation fee according to current product details.
Q: What regions and company sizes are supported?
A: Rolebot supports North America, EMEA, APAC, and South America and is used by organizations ranging from 101 employees to enterprise (10,000+).
Q: What data is transferred into Greenhouse?
A: When you accept a candidate, Rolebot exports contact details (email, phone), social links, and profile metadata to create the Candidate in Greenhouse.
Q: How does Rolebot improve over time?
A: Algorithmic learning uses reviewer feedback (thumbs up/down) to refine candidate rounds for better look‑alikes on subsequent days.
Q: Where can I find privacy and support documentation?
A: Rolebot provides a privacy policy and a Greenhouse support page to outline data handling and integration support steps.
Implementation checklist and typical timeline: 1) Identify pilot roles and two seed profiles for each — 1–2 days to craft representative exemplars; 2) Link Greenhouse jobs in Rolebot and assign user access — a one-time setup that typically takes under an hour; 3) Run a 2–4 week pilot to calibrate feedback and measure initial metrics; 4) Iterate seed profiles and outreach messaging based on acceptance rates.
Troubleshooting and best practices
- Tune seed profiles carefully Use two high-performing employee or candidate profiles per role; slight adjustments to skills/experience filters produce markedly different candidate pools.
- Set reviewer SLAs Commit to reviewing daily rounds within a fixed window to prevent candidate coldness and maximize outreach effectiveness.
- Monitor exports for duplicates Use the GH logo search flow to reconcile prospects in Greenhouse before creating duplicates.
- Track outreach sequencing Review Rolebot’s outreach cadence and customize messaging where possible to match employer brand and response rates.
Example ROI calculation (conservative): assume a team fills 100 open roles per year and manual sourcing consumes 4 hours per role at $60/hour (sourcer cost) = $24,000/year. If Rolebot reduces sourcing time to 0.5 hours per role and improves first-interview conversion, direct sourcing labor savings could be ~ $21,000 annually, plus accelerated time-to-hire that reduces vacancy costs. These figures will vary by role seniority and hiring volume but demonstrate how automated passive sourcing transfers effort from sourcing to quicker evaluation and outreach.
Security and data flow considerations: Rolebot sources public signals from across the web and stores candidate metadata; accepted candidates' contact data is exported to Greenhouse. Review Rolebot’s privacy policy and your internal data retention rules to ensure compliance with regional regulations. The integration includes a search mechanism to check for existing prospects in Greenhouse before creating new records, minimizing duplication risk.
Supported regions and typical company sizes
| Region | Typical company sizes supported |
|---|---|
| North America | 101–1,000; 1,001–10,000; 10,000+ |
| EMEA (Europe, Middle East, Africa) | 101–1,000; 1,001–10,000; 10,000+ |
| APAC (Asia Pacific) | 101–1,000; 1,001–10,000; 10,000+ |
| South America | 101–1,000; 1,001–10,000; 10,000+ |
Speed Up Resume Screening with ZYTHR
After Rolebot surfaces high-quality candidates into Greenhouse, use ZYTHR to instantly screen and rank resumes — saving hours per hire and improving review accuracy so your recruiters focus on the best matches. Try ZYTHR’s AI resume screening to cut screening time and surface top candidates faster.