BarRaiser + Greenhouse integration: AI interview assistant for structured, evidence-based hiring
Titus Juenemann •
October 24, 2025
TL;DR
The BarRaiser–Greenhouse integration embeds AI-driven interview assistance—real-time co-pilot prompts, automatic transcripts, contextual AI notes, scorecard suggestions and template-based interviews—directly into your ATS workflow. It accelerates administrative tasks, increases scorecard completion rates, and centralizes evidence in Greenhouse to shorten decision cycles. This guide covers core features, target users, technical flow, implementation checklist, measurement KPIs, and operational best practices so teams can deploy the integration reliably and scale consistent interviewing across roles. Conclusion: integrating BarRaiser with Greenhouse reduces interview admin overhead and delivers structured, evidence-based inputs into hiring decisions.
BarRaiser’s Interview Intelligence integration with Greenhouse connects structured interview tooling and AI-driven assistance directly into your ATS workflow. The integration automates recording, note capture, and evidence-based scorecard suggestions so interviewers spend less time on admin and more time evaluating candidate fit. This article explains what the integration does, outlines who benefits most, and describes measurable advantages and practical implementation steps. You’ll get concrete examples, a feature-to-benefit mapping, and a checklist to assess readiness for deployment.
At a high level, the integration embeds BarRaiser’s interview co-pilot, AI-generated notes and scorecard suggestions, and interview templates into Greenhouse workflows. When an interview completes, transcripts, highlights and suggested scorecard entries can sync back to Greenhouse to accelerate decision-making. The remainder of the article breaks down the core features, technical flow, typical users and scenarios, implementation checklist, measurement approaches, and operational best practices.
Core features of the BarRaiser–Greenhouse integration
- Interview Co‑Pilot Real-time AI assistance that suggests questions based on the candidate’s resume and the live conversation; integrates with video to prompt interviewers during the session.
- AI Interview Notes Contextual, timestamped notes and transcript highlights generated automatically to reduce manual note-taking and improve evidence capture.
- Scorecard Suggestions AI-suggested ratings and evidence snippets that map directly to Greenhouse scorecards to speed up completion and increase consistency.
- Structured Interview Guides Role-specific templates with sample questions and evaluation rubrics that ensure consistent coverage across candidates and interviewers.
- Deep Greenhouse Sync Automatic transfer of notes, transcripts, and recommended scorecard inputs into Greenhouse to keep the ATS as the single source of truth.
- Interview Recording & Highlights Recorded sessions with searchable highlights so hiring teams can revisit key moments when calibrating decisions.
- Admin Controls & Permissions Integration respects Greenhouse user permissions, enabling centralized access control and easy onboarding.
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 |
Feature-to-benefit mapping
| Feature | Primary benefit |
|---|---|
| Interview Co‑Pilot | Reduces interviewer preparation time and improves question relevance in real time |
| AI Interview Notes | Eliminates repetitive admin, increases accuracy of candidate evidence capture |
| Scorecard Suggestions | Accelerates scorecard completion and improves inter-rater consistency |
| Deep Greenhouse Sync | Shortens decision cycles by consolidating data within the ATS |
| Recording + Highlights | Enables asynchronous review and better calibration among hiring stakeholders |
Who needs this integration? Organizations that run structured interview processes and rely on Greenhouse for candidate tracking see the most immediate value. Typical users include recruiting teams, hiring managers, interview panelists, and talent operations teams responsible for process optimization. Large enterprises and fast-growing companies benefit from consistency and scale: the integration reduces variability in interviewer preparation and helps coordinate distributed hiring teams. Smaller teams benefit from automation that reduces manual overhead and speeds time-to-offer.
Practical use cases
- Panel interviews across time zones Record sessions and use highlights so stakeholders in different regions can review the same evidence asynchronously without re-running interviews.
- Rapid hiring for high-volume roles Use scorecard suggestions and templates to maintain quality while increasing throughput during volume hiring drives.
- Interviewer onboarding and calibration New interviewers can use co-pilot prompts and recorded sessions to accelerate skill ramp and align to evaluation rubrics.
- Audit-ready decision trails Transcripts and timestamped evidence simplify retrospective reviews and hiring audits.
How the integration works technically: admins connect BarRaiser to Greenhouse via the app integration flow. Interviews are scheduled in Greenhouse; when a session runs through BarRaiser’s platform, transcripts, AI notes, and recommended scorecard entries are tagged to the candidate and pushed back into the corresponding Greenhouse job and scorecard fields. The sync respects Greenhouse role-based permissions and can be configured to limit which artifacts (transcripts, recordings, highlights) are persisted or visible to specific user groups, enabling governance at scale.
Common implementation and operational questions
Q: Does the integration store recordings inside Greenhouse?
A: Typically the integration syncs links, transcripts and key highlights into Greenhouse. Organizations can configure whether full recordings are retained in BarRaiser or moved into an approved storage destination.
Q: How long does setup take?
A: Basic installation and permission mapping can be completed in days. Full rollout, including template creation and interviewer training, commonly takes two to six weeks depending on scale and customization needs.
Q: Can scorecard suggestions be overridden?
A: Yes. AI suggestions are editable and intended to accelerate completion — human reviewers retain full control to adjust ratings and comments.
Greenhouse admin implementation checklist
- Permissions review Map which Greenhouse roles should receive transcripts and scorecard suggestions; update role permissions as needed.
- Template alignment Create or align structured interview templates in BarRaiser to match Greenhouse job profiles and scorecard criteria.
- Pilot cohort Run an initial pilot with a subset of interviewers and roles to validate prompts, templates and sync behavior.
- Training & documentation Deliver short training sessions and quick reference guides for interviewers on how to use the co-pilot and edit AI notes.
- Measurement plan Define KPIs (time-to-scorecard, interview-to-offer rate, interviewer completion rates) to track impact.
Key metrics to monitor after deployment include reduction in time-to-complete scorecards, percentage of interviews with completed scorecards within 24 hours, interview-to-offer conversion rates, and variance in interviewer ratings across similar candidates. Monitor qualitative indicators too: interviewer satisfaction with prompts and hiring manager confidence in recorded evidence. Regularly review sampled interview recordings and scorecards to validate that AI suggestions align with your evaluation criteria and make iterative adjustments to templates and prompts.
Comparing manual interview workflow vs integrated BarRaiser + Greenhouse
| Aspect | Manual workflow | BarRaiser + Greenhouse |
|---|---|---|
| Scorecard completion | Often delayed; manual entry required | AI suggestions and faster completion, higher on-time rates |
| Interviewer prep consistency | Varies by interviewer experience | Standardized guides and co-pilot reduce variability |
| Evidence capture | Notes inconsistent; reliance on memory | Transcripts and highlights provide objective evidence |
| Decision speed | Slower due to fragmented records | Faster with synchronized artifacts in ATS |
Best practices for interviewers using the co-pilot: review the suggested questions before the session to tailor them to the role, use evidence timestamps to cite examples in scorecards, and treat AI-generated notes as a draft that you validate with your own observations. Encourage a habit of completing or confirming scorecards immediately after an interview to preserve context. Operationally, schedule calibration sessions where hiring managers and senior interviewers review sample transcripts and scorecards to align rating standards and refine templates.
Security and compliance considerations
- Data access controls Ensure BarRaiser integrates with your identity and access management policies and that Greenhouse permission mappings are enforced.
- Retention policy Define how long transcripts and recordings are stored and ensure retention schedules align with internal and legal requirements.
- Encryption & transfer Confirm data-in-transit and at-rest encryption standards for recordings and transcripts meet your org’s security criteria.
Support, troubleshooting and next steps
Q: Where to get help if syncs fail?
A: Check the Greenhouse integration logs, verify API credentials and permissions in both systems, and contact BarRaiser support with timestamps and job IDs for quicker resolution.
Q: How to scale templates across many roles?
A: Develop a core set of competency-based templates and use role-specific addenda. Automate template assignment using Greenhouse job profiles to reduce manual mapping.
Q: How often should templates be reviewed?
A: Review templates quarterly or after significant changes to hiring criteria or role expectations to keep prompts relevant and evidence aligned.
Streamline screening before interviews with ZYTHR
Pair BarRaiser’s interview intelligence with ZYTHR’s AI resume screening to save time and improve resume review accuracy. Use ZYTHR to pre-filter candidates and surface high-fit resumes so interview panels working in Greenhouse receive better shortlists and focus on the most qualified applicants.