Metaview Integration: Centralize Interview Audio, AI Summaries and Searchable Transcripts in Your ATS

Metaview’s AI scribe plus Greenhouse creates a single source of truth for spoken candidate data: automatic call capture, AI-generated notes, and synchronized records in your ATS. The integration reduces manual note-taking, surfaces structured insights from interviews, and makes interview content accessible directly on candidate profiles. This guide explains exactly what the integration does, who gains the most from it, and the measurable benefits you can expect—plus implementation steps, compliance considerations, KPI suggestions, and troubleshooting tips for smoother adoption.

Core capabilities: What the Metaview–Greenhouse integration does

• Automated call capture Records interviews and recruiting calls automatically (based on configured triggers) and sends audio to Metaview for processing—no manual upload required.
• AI-generated interview notes Produces concise, role-relevant summaries that extract competencies, candidate signals, and decision points, then attaches them to the corresponding Greenhouse interview or candidate profile.
• Two-way metadata sync Syncs interview metadata—dates, interviewer, stage, job—and makes Metaview notes available inside Greenhouse, ensuring each candidate record contains complete conversation history.
• Access and permissions alignment Maps team-level permissions so notes visibility follows Greenhouse access settings; admins can manage who sees sensitive content.
• Searchable transcript and reporting Stores transcripts and provides filters and reports so teams can discover trends across interviews and identify recurring hiring signals or risks.

How the integration operates technically: once enabled, Greenhouse triggers send call metadata to Metaview and the platform captures audio from configured meeting platforms or phone systems. Metaview processes the audio with its speech-recognition and purpose-built recruitment models, returning structured notes and transcripts to Greenhouse via the integration. Data flow is typically: meeting/phone capture -> secure audio transfer -> AI processing (summaries, tags, timestamps) -> notes/transcripts posted to a Greenhouse candidate/interview record. Teams can tune what gets posted and who has access to each piece of content.

Feature-to-benefit mapping

Who benefits most from this integration

• High-volume recruiting teams Teams hiring for many roles or running frequent screens can reduce time spent on note-taking and scale consistent evaluation.
• Distributed interview panels Hiring managers and interviewers across time zones get the same context without synchronous debriefs.
• Talent leaders and ops Provides analytics and conversation-level reporting to identify process gaps and interviewer variance.
• Compliance- or audit-sensitive organizations Transcript and note retention help with audit trails when interviews must be recorded for review.
• SMBs to enterprise Integration scales from small teams to 10,000+ employees—configurations and permissions adapt to company size.

Typical use cases and concrete examples: a recruiter running 20 screening calls per week can have Metaview attach a 3–5 sentence summary to each Greenhouse application, cutting manual note time from 6–8 minutes per call to under one minute for review. Hiring managers who join final-stage interviews can read AI summaries ahead of panels to make faster, evidence-based decisions. Other examples include quality assurance (QA) audits of hiring conversations, root-cause analysis when candidate drop-off rises, and enabling sourcers to quickly review call outcomes to prioritize outreach.

Key benefits and measurable outcomes

• Time savings Automated notes reduce manual note-taking and debrief time—teams typically reclaim hours per week per recruiter.
• Improved decision accuracy Structured summaries and timestamps reduce information loss and interviewer bias from rushed notes.
• Faster handoffs Hiring managers and interviewers see uniform summaries in Greenhouse, accelerating stage decisions and offer timelines.
• Actionable reporting Conversation analytics reveal common concerns, skill gaps, and interviewer consistency—informing training and process changes.
• Scalable documentation Centralized, searchable records let teams onboard new interviewers faster and keep historical context for rehires or role audits.

Implementation checklist and typical timeline

Permissions, privacy, and language support: Metaview supports a broad set of languages and regions, and the Greenhouse integration allows teams to enforce visibility controls so only authorized users see transcripts and notes. Before enabling call capture, confirm local consent and recording laws for the regions where interviews occur; this is a standard step in the implementation checklist. Metaview provides privacy documentation and integrates with Greenhouse’s user roles so admins can map access. If your organization requires specific retention periods or redaction, include those requirements during pilot configuration.

Common pitfalls and troubleshooting tips

• Low-quality audio Ensure participants use headsets and stable connections; poor audio degrades transcription accuracy—log and address recurring sources of noise.
• Incomplete metadata mapping Double-check job, stage, and interviewer fields in Greenhouse so notes attach to the correct candidate records.
• Mismatched access controls Confirm that Metaview visibility settings mirror Greenhouse roles to prevent accidental overexposure of notes.
• Overloading interviewers Avoid sending raw transcripts to large groups; use tailored summaries for busy hiring managers and preserve transcripts for deeper reviews.

Frequently asked questions

KPIs to track after activation: monitor average time spent on interview notes per candidate, interview-to-offer conversion by hiring manager, time-to-hire, and frequency of re-interviews caused by missing context. Use initial baseline measurements during the pilot and establish target improvements (for example, 50% reduction in manual note time or a 10% improvement in interview-to-offer conversion). Combine conversation analytics with existing ATS metrics to quantify the integration’s impact on speed, quality, and recruiter capacity.

Recommended workflows to operationalize the integration

• Screening + auto-summary Capture screening calls, let Metaview post a short summary to Greenhouse, and allow sourcers to triage follow-ups directly from the candidate profile.
• Panel prep with AI briefs Before panel interviews, attendees read Metaview summaries of prior conversations to align focus and reduce redundant questioning.
• QA and interviewer calibration Periodically sample transcripts in Metaview reports to calibrate scoring rubrics and interviewer consistency across roles.
• Handoff to hiring managers Attach a clear decision checklist and AI summary to candidate records so hiring managers can make faster, evidence-based choices.

Sample report widgets to monitor in Metaview + Greenhouse