Metaview for AI Interview Notes

Primary intent: eliminate manual note-taking from recruiting interviews using Metaview's AI-powered note-taking platform as of early 2026. Metaview joins your video call (Zoom, Google Meet, Microsoft Teams), transcribes the conversation, and generates structured notes organised around the competencies or question sets you define. The notes flow into your ATS, typically Greenhouse, Lever, or Ashby, so scorecards are populated from the recording rather than from memory reconstructed an hour after the call.

The platform was built specifically for recruiting, which is the clearest reason to choose it over general meeting intelligence tools. A Fireflies.ai or Otter.ai transcript surfaces action items and topic headings; Metaview surfaces competency evidence, candidate quotes mapped to your interview guide, and structured fields that match your scorecard. The difference matters when a hiring manager asks why a candidate advanced or did not, and the note in the ATS needs to be defensible, not just present.

If your question is whether Metaview is the right tool versus other interview recording or AI note-taking options, read How it compares to similar tools below. If you already have access and want to configure your first live template, go straight to Practical steps.

Data governance, including candidate consent, recording disclosures, and retention policy, is a legal and security question before it is a feature question. Metaview integrates natively with major ATSs and video conferencing platforms, but the compliance setup must happen before the first recording. Broader interview workflow context: HireVue for asynchronous video screening, Calendly for interview scheduling, ChatGPT for debrief summaries from exported notes.

Pick your interview intelligence tool against your actual workflow: whether your notes problem is consistency, speed, ATS friction, or interviewer preparation varies the right answer.

Where to start (opinionated): if your team runs structured interviews against a defined competency framework and uses Greenhouse, Lever, or Ashby as your ATS, Metaview is the narrowest, most recruiting-shaped fit. If your note problem is more about getting any notes at all rather than structured ones, Fathom or Otter.ai cost less and are faster to deploy without IT approval cycles. If budget is zero and you already use ChatGPT, a pasted transcript plus a well-structured prompt gets you 70% of the output without adding a new vendor to your security review queue.

A first live interview with Metaview: under 30 minutes from setup to ATS note

1. Install and connect. Add the Metaview Chrome extension or connect your Google or Microsoft calendar. Authorise the video conferencing integration (Zoom, Google Meet, or Teams) from the Metaview settings panel.

2. Link your ATS. In Metaview settings, connect to Greenhouse, Lever, or Ashby via the native integration. Confirm the OAuth scopes cover scorecard write access, not just read.

3. Create a note template. Match the headings to your existing scorecard fields. If your Greenhouse scorecard has "Problem solving", "Stakeholder communication", and "Role-specific technical skill", create Metaview headings with the same names so the mapping is unambiguous and field population is automatic.

4. Run a practice interview internally. Record a 10-minute mock interview with a colleague before using Metaview on a live candidate. Review the AI notes: are the speaker labels correct, are the competency headings being populated, and is the transcript accurate enough to quote?

5. Set candidate consent language. Add a recording disclosure to your interview invitation: something like "Our interviews are recorded for note-taking purposes using Metaview. Recordings are used only to generate structured notes and are deleted within [X] days." Have your legal team confirm the wording before it goes to candidates.

6. Run the live interview. Metaview joins automatically when the meeting starts. Focus on the conversation. Drop a quick inline note in the Metaview sidebar if you want to flag a moment the AI might have missed.

7. Review and edit the AI draft before submitting. Open the generated notes in Metaview immediately after the call. Spend five minutes: correct any transcription errors, add context for anything the AI missed, and delete placeholder text not supported by what was actually said.

8. Push to the ATS. Use the Metaview to ATS integration to populate the scorecard. Confirm the fields mapped correctly before saving. Add a one-line hiring recommendation (advance, hold, decline) that reflects your own judgement, not the AI summary.

Optional: debrief summary from panel notes using Claude or ChatGPT

If you are preparing a hiring manager debrief, export the Metaview notes for each panellist and paste them into Claude or ChatGPT with the second prompt below. Do not paste notes from multiple candidates in one session.

Second prompt: debrief summary from panel Metaview notes

You are helping a recruiter prepare a hiring manager debrief summary. Use only the notes below. Do not infer, estimate, or add context not present in the text. If a panellist's notes are too thin to draw a conclusion, write INSUFFICIENT EVIDENCE.

CANDIDATE (name or anonymised identifier):
[paste]

ROLE BEING HIRED:
[paste: role title, level, must-have outcomes for the first 90 days]

PANELLIST NOTES (paste each panellist's Metaview notes separately, labelled by interviewer name or role):
[paste]

Output exactly these sections:
1) Competency summary table (one row per competency; columns: Competency | Evidence for | Evidence against | Panel consensus)
2) Key strengths (2-3 bullets; direct quotes from notes; label which panellist observed this)
3) Key risks or gaps (2-3 bullets; direct quotes; label which panellist observed this)
4) Suggested debrief discussion points (questions the panel should resolve before making a decision)