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Glossary

AI clip extraction (from a screening call)

AI clip extraction is the process of taking a transcribed screening-call recording and using a language model to identify the candidate moments most relevant to the role's evaluation rubric, returned as start/end timestamps with a short rationale.

Updated June 15, 2026

Short definition

AI clip extraction is the process of taking a transcribed recruiter screening-call recording and using a frontier language model to identify the candidate moments most relevant to the role's evaluation rubric. The output is a list of clip ranges (start and end timestamps), each labeled and tied to a specific scorecard requirement. The recruiter typically reviews and approves clips before they attach to a candidate submission.

When a recruiter conducts a screening call with a candidate, the recording typically runs 30-60 minutes. The hiring manager who needs to evaluate the candidate has, in practice, two or three minutes of attention to give. The gap between the call length and the HM's attention budget is where AI clip extraction earns its keep — by pre-curating the candidate moments most worth watching.

How the pipeline typically works

AI clip extraction has three steps: capture, transcribe, extract.

  • Capture: an AI notetaker joins the meeting and records the audio and video.
  • Transcribe: the recording is transcribed with timestamps, typically word-level or utterance-level.
  • Extract: a language model reads the transcript against context (the role's scorecard, the candidate's resume, the recruiter's notes) and proposes the top N moments as clip ranges.

Why the rubric matters

Without a rubric, the model has to guess what counts as a relevant moment. The output tends toward generic claims of expertise — "the candidate talks about Python" — that are technically true and practically useless.

With a rubric (the role's scorecard requirements), the model has a target. It can flag the specific moment where the candidate explains how they scaled Postgres write throughput at a fintech, and tie that clip directly to the "production distributed systems" requirement. The recruiter and the hiring manager both see the line from rubric to evidence.

The recruiter-in-the-loop pattern

Mature AI clip extraction pipelines hold an explicit recruiter-approval gate before clips publish. The model proposes; the recruiter approves, edits, or rejects. This pattern matters for three reasons: it catches model errors before they reach the candidate's prospective employer, it preserves the recruiter's editorial voice on the submission, and it gives the recruiter a feedback signal that can tune the pipeline over time (rejection reasons feed back into prompt iteration).

Where Submio fits

Submio ships AI clip extraction on every plan, including Free. The recruiter sends the Submio Notetaker to their screening call, the model extracts the top three moments mapped to the role scorecard, and the recruiter approves each clip before it attaches to the submission and renders on the public HM review page. The full call recording stays in the recruiter's account; only the approved clip windows are exposed to the hiring manager.

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