How Jump creates high-quality meeting transcripts

Who can use this feature?

Full users on Ramping, Core, and Scale (legacy)
Full users on Meet
Lite users who are Team admin

What are meeting transcripts?

A meeting transcript is a timestamped, written record of what was said in a meeting.
In Jump, transcripts are the source material used to generate structured meeting outputs.
Higher-quality transcripts help Jump create clearer notes, more reliable action items, and more accurate CRM-ready updates.

Why transcripts matter in Jump

Jump generates notes, tasks, follow-ups, and CRM-ready outputs by reasoning from the transcript.
Transcript quality directly impacts downstream quality—cleaner transcripts reduce confusion and capture more detail.
Speaker-aware transcripts matter in financial advice conversations, where it’s important to know who said what (advisor vs. client vs. staff).

How Jump creates high-quality transcripts

At a high level, Jump:

Captures or receives meeting audio.
Converts audio into a timestamped transcript (speech-to-text).
Separates speakers where possible (speaker diarization).
Uses meeting and attendee context to label speakers (speaker identification).
Produces a canonical transcript.
Uses that transcript to generate notes, tasks, follow-ups, CRM updates, and other meeting intelligence.

Speech-to-text quality

Jump uses an industry-leading speech-to-text model designed for real-world conversation, including:

Longer meetings
Financial terminology
Back-and-forth dialogue, interruptions, and multi-speaker discussions

This matters because advisor meetings are rarely “clean scripts.” Stronger transcription creates better raw material before any AI note-taking or CRM automation begins.

Speaker diarization

Speaker diarization is the process of separating different voices in a transcript—identifying when one person stopped speaking and another started.

This is critical for making transcripts usable:

Without speaker separation, a meeting can become one long block of text.
With speaker separation, Jump can structure the conversation, make it easier to review, and generate better downstream outputs.

Example:

If a client says, “We want to update our beneficiaries,” and the advisor says, “I’ll send you the paperwork,” diarization helps Jump treat those as different speakers so it can create the right follow-up.

Speaker identification

Speaker diarization separates voices. Speaker identification tries to connect those voices to actual people.

Jump uses available meeting context to identify speakers where possible, including:

Calendar attendees
Meeting participants
CRM contacts
Meeting platform data

Speaker identification is easiest when each participant joins from their own device. It can be more difficult when:

Names from meeting platforms are unclear
Attendees are missing
People join from shared devices
A conference room microphone represents multiple people

Shared microphones and multi-speaker streams

A common challenge is the shared microphone problem, where multiple people speak through one device (for example, spouses on one laptop or a conference room microphone).

To the meeting platform, this may appear as one participant and one audio stream—even though multiple humans are speaking. If the system blindly trusts platform identity in these situations, speakers can be merged, which can degrade transcripts and downstream meeting outputs.

Jump invests in shared microphone recovery because these cases are common in financial advice meetings.

Hybrid speaker recovery

Jump’s approach follows a simple principle: keep what is known to be correct, and intelligently improve what is ambiguous.

Platform identity can be highly reliable when one person joins from one device.
Machine diarization can separate voices from audio but may not know who those voices belong to.

Jump combines these signals:

Preserving platform-based identity when it appears reliable
Detecting when a single stream likely contains multiple voices
Applying additional diarization and labeling logic to separate and resolve speakers where possible

The goal is not perfect identification in every meeting. The goal is the best possible transcript without making already-correct speaker labels worse.

Retranscription and reprocessing

In some cases, the best transcript can only be created after Jump has more context.

Where supported, Jump can retranscribe or reprocess a meeting so the transcript and downstream outputs can be regenerated from better source data—especially when speakers were merged or attendee context was incomplete.

When a meeting is retranscribed or reprocessed, Jump may clear and regenerate downstream meeting artifacts (such as notes, insights, topics, scorecards, and un-synced tasks) while preserving important completed or CRM-synced work where appropriate.

What users should expect

Transcript creation runs automatically as part of meeting processing.
In typical meetings, Jump creates a readable transcript, separates speakers where possible, and uses the transcript to generate high-quality notes and action items.
In complex meetings (especially shared microphones or unclear attendee context), users may need to review speaker labels or meeting outputs.

No transcription system can guarantee perfect speaker attribution in every meeting. Audio quality, overlap, background noise, microphone setup, and missing attendee context can all affect transcript quality.

Best practices for better transcripts

Jump’s transcript engine is powerful, but audio quality still matters.

For best results:

Ask each remote participant to join from their own device when possible.
Use a clear microphone and reduce background noise.
Avoid talking over other speakers.
Make sure the attendee list is accurate (especially when spouses, staff members, or additional client family members are present).

These small steps help Jump create cleaner transcripts, better speaker labels, and stronger downstream meeting outputs.

FAQ

Do I need to turn anything on?

No. Jump’s transcript creation process runs automatically as part of meeting processing.

Does Jump use speaker diarization?

Yes. Jump uses speaker diarization to separate different voices in a meeting transcript where possible.

Does Jump know who is speaking?

Jump uses available meeting, attendee, participant, and CRM context to identify speakers where possible. Speaker identification is easiest when each participant joins from their own device and attendee information is accurate.

Why are shared microphones hard?

Shared microphones are hard because the meeting platform may treat multiple people as one participant. For example, if a husband and wife join from the same laptop, the platform may only see one audio stream. Jump may need additional diarization and labeling logic to separate those speakers.

Can Jump fix every shared microphone meeting?

Not always. Shared microphone recovery depends on the meeting source, audio quality, available audio artifacts, and whether the voices can be reliably separated. Jump is designed to improve these cases, but users should still review important outputs when the meeting setup was complex.

Will better transcripts improve my notes?

Yes. Jump generates notes, tasks, follow-ups, and CRM-ready outputs from the transcript. Better transcripts give Jump better source material, which improves downstream quality.

What should I do if a speaker label looks wrong?

Review the attendee list and speaker labels. If reprocessing or retranscription is available for the meeting, updating attendee context and regenerating the meeting outputs may improve the result.

Are transcripts perfect?

No transcription system is perfect. Transcript quality can be affected by background noise, overlapping speech, low microphone quality, unstable internet, accents, unclear audio, or multiple people speaking through one device. Jump’s goal is to produce the highest-quality transcript possible and continue improving the hardest real-world cases.

Need More Help?

Chat: In your Jump account, click Help > Chat with support.
Email: [email protected]